3.1 Research Axis 1: Vision and 3D Perception for Scene Understanding

Navigation for mobile robotics requires a robust understanding of the environment from 2D or 3D sensors. Recent learning-based vision algorithms are now able to operate in highly cluttered environments, and tasks which were considered challenging — such as semantic segmentation or object detection — are soon to be solved to a certain extent. Still, the classical supervision paradigm, which relies on large annotated datasets, cannot encompass in practice all outdoor conditions and scenarios. There is therefore a need both to relax the requirement of massive annotations and to extend the perception capability to situations unseen or rarely seen in the training data.

To that aim, in this research axis, we investigate several broad topics. First, we transversely investigate learning with less supervision with applications to various perception tasks. Focusing on outdoor vision, we conduct research relying on data-driven or physics-guided paradigms to hallucinate complex lighting/weather conditions and compensate for missing data in the training sets. Because mobile robots evolve in the physical world we also investigate how vision algorithms can provide in-depth 3D understanding of the scene from images and/or LiDAR scans.

To evaluate our research as well as to foster reproducibility, we rely on relevant recent public datasets (nuScenes  48, Waymo Open  139, Woodscapes  150, SemanticKITTI  39, CADCD  126, etc.) and intend to openly share our research results.

3.2 Research Axis 2: Localization & Mapping

Vehicle localization and environmental mapping are pillars of the perception task for an autonomous vehicle. While vehicle localization ensures the global positioning of the vehicle in its environment and local positioning with regard to the road and to the close road features, environment mapping contributes in building a useful internal representation that is exploited by the decision system.

Inria and Valeo teams have been working - separately and jointly - on the localization and mapping solutions for over the past 15 years. Many algorithms have been developed and showed their effectiveness in terms of accuracy, precision and safety expectations for autonomous driving. However, the integrity, safety, data size and costs are still challenging points that ASTRA wants to address.

3.3 Research Axis 3: Decision making, motion Planning & vehicle Control

Decision-making, maneuver and motion planning, and vehicle control are extremely vital components of the intelligent vehicle. These modules act as a bridge, connecting the perception subsystem of the environment and the bottom-level control subsystem in charge of the execution of the motion. We address these issues covering various strategies of designing the decision-making, trajectory planning, and tracking control, as well as shared driving of the human-automation to adapt to different levels of the automated driving system accounting with the driver profile.

The challenges related to decision making and path planning are mainly related to four distinct elements:

1. Errors and uncertainties introduced by the perception subsystems
2. Environment static and dynamic occlusions
3. Lack of understanding and prediction of other road users behaviors
4. Simultaneous consideration of several constraints related to: vehicles dynamics, energy consumption, passengers comfort, offense to driving rule...

Different approaches are investigated in the state of the art addressing one or several issues but, to our knowledge, none are capable of addressing all of them simultaneously. More specifically in most approaches decision and planning are dealt separately or in a way that favors one of them. Approaches based on Markov decision process (MPD, POMDP,...), path-speed profiles, ontologies, artificial potential fields coupled to MPC controllers are able to show interesting results in dedicated environments or in specific situations, however most of them do not tackle properly specific issues such as intention and behavior predictions, interactions or multi-criteria real time optimal maneuver decision.

While continuing the investigation of end-to-end driving approaches based (inverse-)reinforcement learning decision-making approaches, we keep on improving current path-planning methods already developed by both teams at RITS and DAR: Reachable Interaction Sets 38, Artificial Potentials Fields (coupled to MPC control) which are designed for obstacle avoidance, as well as traditional path planning methods. Optimal methods based on the convex optimization and cubic splines are investigated at DAR to design optimized and robust trajectories. More specifically, we are mainly focusing on the following three scientific topics (detailed in the next sections):

• Maneuvers and trajectories prediction of surrounding road users
• Schemes for ego-vehicle actions and maneuvers decision making and motion planning
• Motion planning and trajectories generation

3.3.1 Maneuver and trajectory prediction

To achieve a safe and comfortable driving, an autonomous driving system must have an accurate knowledge of the future motions of all other traffic agents surrounding the autonomous vehicle, such as cars, pedestrians, cyclists, etc. Motion prediction is thus a key task in autonomous vehicles. Several methods of motion prediction have been studied in the literature. Lefèvre et al 103 propose their classification in three levels with an increasing degree of abstraction: Physics-based models, Maneuver-based models and interaction-based models.

• Physics-based motion models. They consider that the motion of vehicles only depends on the laws of physics. The future motion is predicted using dynamic and kinematic models linking some control inputs car properties and external conditions. These models are limited to short term prediction and are unable to anticipate any change in the motion of the car caused by the execution of a particular maneuver.
• Maneuver-based motion models. They consider that the future motion of a vehicle also depends on the maneuver that the driver intends to perform. The future motion of a vehicle on the road network corresponds to a series of maneuvers executed independently from the other vehicles. These models are Unadaptable to different road layouts.

• Interaction-aware motion models. They take into account the inter-dependencies between vehicles’ maneuvers. These models require computing all the potential trajectories of the vehicles which is computationally expensive and no compatible with real-time risk assessment. Valeo has filed a patent to overcome this issue 146. This patented method is being developed in order to be tested in the automated driving prototypes.

  Fig. 2 shows a comparison of the different models including their challenges and the used algorithms.

Valeo has considered these categories in its development of the automated driving prototypes Cruise4U and Drive4U. The physical-based model is used in situations when their is no knowledge about the route geometry (for example in a big roundabout without lanes), the maneuver-based in highway and urban environment when the road topology is available from HD Map or valeo Drive4U Locate map.

In the few last years, machine learning based algorithms and particularly deep learning are used in order to solve the limits of the current prediction methods. Human motion trajectory prediction has been addressed in the literature 44, 134. A large amount of naturalistic road user trajectories in different contexts (highways 57, 58, 93 or urban 48, 50) needed to train and evaluate deep learning methods are now available. Our first works 10, 117,9, 115, taking as input the track history of a target vehicle and of its surrounding moving road users, obtained accurate prediction results of the target vehicle motion on highways and an extension 116, including the static scene structure, has been proposed for an urban context. Valeo is involved in this research area with activities in prediction of other road users and ego-vehicle trajectory. Different approaches have been implemented and tested in simulation and on test cars 47, 46.

However, work has still to be done in this domain in terms of performance, robustness and generalization before being used in real autonomous driving applications. In fact, the behavior of a human driver depends also on the contextual knowledge of the environment (speed limits, traffic density, day of the week, visibility, road equipment, driver's country, etc.) and on its goal 154. We plan to include these contextual cues in a prediction method, which should also compute multiple plausible trajectories representing the driver's diverse possible behaviors, give uncertainties estimations on the predictions, carry out multi-agents trajectory forecasts and should be usable in any environment. It will necessitate the use of a more complete dataset 153 composed of various driving scenarios collected from different countries, which may be completed by our own dataset collected with the help of Valeo if necessary. This work will be done in collaboration with Itheri Yahiaoui from Reims University and within the starting PhD thesis of Amina Ghoul funded by the SAMBA project.

3.4 Research Axis 4: Large scale modeling and deployment of mobility systems in Smart Cities

While axes 1 to 3 deal with subjects related to the on-board intelligence of an “individual” intelligent vehicle and its autonomous navigation, axis 4 intervenes when it comes to many communicating, autonomous or automated vehicles but also when it comes to the cooperation with the static environment (infrastructure). The latter may contain and integrate: roadside and monitoring sensors (Cooperative Perception Services), signaling, communication infrastructures, cloud... The research concerns in particular the deployment of equipped vehicles on a large scale in a road or urban environment.

The research objectives are twofold.

• First, the focus is on the modeling of systems comprising a large number of vehicles, often seen as random entities.

  The methodology is mainly to explore the links between large random systems and statistical physics. This approach proves very powerful, both for macroscopic (fleet management  62) and microscopic (car-level description of traffic, formation of jams  69, 136, 74, 73) analysis. The general setting is mathematical modelling of large systems (typically in the so-called thermodynamical limit), without any a priori restriction: networks, random graphs, etc. One often aims at establishing a classification based on criteria of a twofold nature: quantitative (performance, throughput, etc) and qualitative (stability, asymptotic behavior, phase transition, complexity).

• The second objective concerns the cooperation of these communicating entities in order to address the efficiency and safety of mobility. This cooperation takes several forms. Direct or indirect communications (V2X) are dedicated to maneuver coordination, taming and improving traffic efficiency (cf. section 4.4.2), platooning, safety critical distributed coordination (cf. 4.4.3)... Crowdsourcing is another aspect that could be used for traffic modeling and prediction (cf. 4.4.1), environment augmented mapping, or global vehicles localization. A Phd student will be hired this year to work on this precise subject (cf. 4.5).

Beside this core methodology, other past activities of interest include discrete event simulation  54, 98 and resource allocation for ITS  97, 81, 82.

Finally, axis 4 does not represent a structural unit like the other axes. Its objective is to deal with the problem of scaling, deployment and multi-vehicle cooperation in a global and systemic way. On the substance, methods and theories of modeling will be investigated and the design of secure telecommunication systems will be elaborated. These models and systems are intended to be implemented in more global systems and architectures. They will interact with the other axes through these architectures and will respond in a targeted way to needs; for example, whenever a need for probabilistic modeling is expressed (e.g. section 4.5).

New softwares

• MonoScene
  Monocular 3D Semantic Scene Completion

  • Url: MonoScene
  • Contribution: leader
  • Software Family: research
  • Audience: community
  • Evolution and maintenance: basic
  • Duration of the Development: $\ge 1$
  •   
• SceneRF

  Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields

  • Url: SceneRF
  • Contribution: leader
  • Software Family: research
  • Audience: community
  • Evolution and maintenance: basic
  • Duration of the Development: $\ge 1$
  •   
• PODA

  Prompt-driven Zero-shot Domain adaptation

  • Url: PODA
  • Contribution: leader
  • Software Family: research
  • Audience: community
  • Evolution and maintenance: basic
  • Duration of the Development: $\ge 1$
  •   
• ManiFest

  Few-shot image translation method working on unstructured environments

  • Url: ManiFest
  • Contribution: leader
  • Software Family: research
  • Audience: community
  • Evolution and maintenance: basic
  • Duration of the Development: $\ge 1$
  •   
• DenseMTL

  Cross-task Attention Mechanism for Dense Multi-task Learning

  • Url: DenseMTL
  • Contribution: leader
  • Software Family: research
  • Audience: community
  • Evolution and maintenance: basic
  • Duration of the Development: $\ge 1$
  •   
• COARSE3D
  Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation

  • Url: COARSE3D
  • Contribution: leader
  • Software Family: research
  • Audience: community
  • Evolution and maintenance: basic
  • Duration of the Development: $\ge 1$
  •   

6.1 New software

6.2 New platforms

Participants: Paulo Resende, Benazouz BRADAI, Gaëtan Le Gall, Fawzi Nashashibi.

The creation of the team has resulted in the strengthening ofthe experimental side of the team which has always had among its objectives the validation of work on real instrumented platforms. The creation of the team has resulted in the strengthening of the experimental side of the team which has always had among its objectives the validation of work on real instrumented platforms. Thus, the team is equipped with at least four road vehicles (Cruise4U and Drive4U from Valeo, a C1 and a Zoé from Inria), 3 shuttles (2 Cybus from Inria and a NAVYA from Valeo) and 2 Cybercars (Inria) ( 6).

7.1 Learning vision with less supervision

Participants: Andrei Bursuc, Anh-Quan Cao, Raoul de Charette, Mohammad Fahes, Ivan Lopes, Patrick Pérez, Tuan-Hung Vu.

Supervision is an evident bottleneck for computer vision in the real-word. In this research axis we have explored new paradigms for relaxing the need of human supervision. We explored the use of multi-modal inputs (here, image and 3D data) to discover statistical correlations between 2D and 3D which we have shown is beneficial for a wide variety of transfer learning scenarios in semantic segmentation, leading to a T-PAMI publication 20. Multi-tasks learning, where a single network solves tasks of different natures, also alleviate supervision burdens because it lowers the need of task-specific architecture. In that sense, we proposed a novel task-exchange mechanism which helps knowledge distillation 26, and demonstrates state-of-the-art results on several datasets. To reduce the amount of supervision, in the 3D axis, we employed constrative learning to build rich and compact representations of semantic classes for 3D semantic segmentation which led to 25, and demonstrated performance to full-supervision (i.e., 100% labels) using only 0.01% labels. Finally, in the latest months we have explored interaction of language and vision models to perform zero-shot adaptation from textual prompt which drives the adaptation of the image features and our experiments show exciting state-of-the-art results 32.

7.2 Vision in complex conditions

Participants: Raoul de Charette, Fabio Pizzati.

Because the physical world is a continuum, encompassing all conditions in a single dataset is impossible. In this direction, and also related to weakly supervised algorithms, we investigated how human-informed knowledge can benefit source-to-target transfer learning, for example to perform on adverse lighting/weather conditions unseen during training. With minimal geometric human-guidance and a patch-wise learning, we demonstrated state-of-the-art results on multiple scenarios 23, which was rewarded Best Paper Award at VISAPP 2022. In a separate work, we also investigated the realistic translation of images (image-to-image translation or, i2i) using of a subsidiary domain, which we call anchor, to learn i2i from only few-shots. In particular, this was applied to the complex day-to-night, or clear-to-rain translations 27. When target domain includes `occlusions' such as raindrop in clear-to-rain, the translations lack realism, and as a continuation of prior works we explored the use of physics models to disentangle visual representations and obtain realistic and controllable translations with GANs 33 (journal submission). In this axis, most results were within the thesis of Fabio Pizzati  128 (defended on Nov 29th 2022).

7.3 3D scene understanding

Participants: Anh-Quan Cao, Raoul de Charette.

Mobile robots require a holistic understanding of the scene, in particular of its geometry. In this research axis, we investigated how 3D perception is improved. Correlated to the weakly-supervised axis, we explored the learning of compact feature representations with just a tiny split of the complete labels in the context of 3D semantic segmentation of point cloud 25. An other important aspect, is the ability to complete the scene geometry and semantics beyond the inevitable scene occlusions. We studied how complete 3D scene semantics can be extracted from a single 2D image, in particular by proposing a new 2D-3D network bridging strategy, leading to MonoScene – the first monocular semantic scene completion method – published in 22. Inspired by these results, in our work, SceneRF, we studied 3D scene reconstruction from a monocular image but this time without any human supervision – building on Neural Radiance Fields (NeRF) to hallucinate novel depth views of a scene from image sequences, leading to 31.

7.4 Trust Management Framework for Misbehavior Detection in Collective Perception Services

Participants: Jiahao Zhang, Fawzi Nashashibi.

This work has been conducted in collaboration with external partners from IRT-SystemX Institute: Ines Ben-Jemaa and Francesca Bassi.

Cooperative Intelligent Transportation System (C-ITS) is a new technology that aims to reduce traffic accidents and improve road safety. The technology relies on wireless communications in the form of safety messages. Collective Perception Messages (CPM) enable vehicles to share their perceived objects with their neighbors in V2X network. These perception data extend local vehicles’ perception and consequently improve road safety awareness. However, attacks on perception data are challenging and require advanced and efficient misbehavior detection mechanism especially in specific road scenarios where contradictory information need to be analyzed. In this work, we introduce a trust management framework  28 to detect misbehaving nodes through transmitted CPM messages. Our framework is based on trust assessment built through several processing steps. It addresses conflict situation when contradictory data are received using the Subjective Logic mechanism. The results show that our solution is effective in detecting misbehaving nodes based on their attributed trust scores. In addition, we show the impact of our solution and some CPM configuration parameters on safety services and especially on risk anticipation in intersection scenarios. As a future work, we plan to evaluate our solution taking into consideration more advanced parameters such as perception uncertainty and more complex attacks on new road scenarios.

7.5 Motion planning and prediction

Participants: Nelson De Moura, Fernando Garrido, Paulo Resende.

Guided by the findings in the recent survey of some team members 19, we have investigated dynamic motion planner as well as studied generation of speed profiles in a path-speed decoupled fashion. With the PostDoc Nelson De Moura (started in Aug. 2022), we also studied: 1) interaction-aware motion predictions with behavior clustering, 2) high-level decision making with Partially Observable Markov Decision Process (POMDP). Initial results on the inD datasets, using k-means clustering, show emergence of category-specific patterns, which could be later employed for fine-grained prediction of traffic users. Some of the results led to experiments on Valeo prototypes (eDeliver4U) on test sites, as show in 7.

7.6 Control and Human Factors

Participants: Nelson De Moura, Tiago Rocha.

Following prior member works 119, 61, we studied a risk prediction system to evaluate the trade-off between ethical aspects and fuel-efficiency improvements in platooning systems. Leveraging the harm concept from 119, an MDP approach is used to adjust dynamically controllers during platooning operation.

7.7 Risk-averse reinforcement learning algorithm for autonomous driving

Participants: Yacine Ben Ameur, Fawzi Nashashibi, Anne Verroust-Blondet.

In a different effort to contribute to autonomous vehicles decision making, a new Reinforcement Learning-based approach has been investigated using surrogate-based optimization to prioritize safety by maximizing performance in worst-case scenarios. The approach is compared to the commonly used risk-neutral PPO algorithm and shows improved results in avoiding collisions while still managing to navigate dense traffic. The algorithm uses a quantile utility network to predict the sum of rewards and compute the gradient w.r.t. policy parameters. Policy parameters and observed utility are stored in a replay buffer and used to train the utility network. This risk-averse approach is a step forward in ensuring the safety of autonomous vehicles. This work has been recently submitted for publication in IEEE ARSO conference.

7.8 A Lightweight Goal-Based model for Trajectory Prediction

Participants: Amina Ghoul, Kaouther Messaoud, Itheri Yahiaoui, Anne Verroust-Blondet, Fawzi Nashashibi.

Predicting the future motion of a dynamic agent knowing its past trajectory is crucial in many fields such as advanced surveillance systems and autonomous vehicles. However this task is challenging as it depends on various factors such as the agent’s intention, the static environment around the agent, the interaction with other agents and its kinematics. Because of these uncertainties, future motion of agents are inherently multimodal. We present a lightweight goal-based model for multimodal, probabilistic trajectory prediction for urban driving. Previous conditioned-on-goal methods have used map information in order to establish a set of potential goals and then complete the corresponding full trajectory for each goal. We instead propose two original representations, based on the agent's states and its kinematics, to extract the potential goals. We conduct a comparative study between the two representations. We also evaluate our approach on the nuScenes dataset, and show that it outperforms a wide array of state-of-the-art methods. The results can be found at the ITSC 2022 Conference 24.

7.9 Stability and String Stability of Car-following Models with Reaction-time Delay

Participants: Guy Fayolle, Jean-Marc Lasgouttes.

In 17, in collaboration and C. Flores (Institute of Transportation Studies, UCB), we investigate the transfer function emanating from the linearization of a car-following model for human drivers, when taking into account their reaction time, which is known to be a cause of the stop-and-go traffic phenomenon. This leads to a non rational transfer function, implying nontrivial stability conditions which are explicitly given. They are in particular satisfied whenever string stability holds. It is also shown how this reaction time can introduce a regime of partial string stability, where the transfer function modulus remains smaller than 1, up to some critical frequency. Conditions are explored in the parameter space discriminating between 3 different regimes (stability, string stability, partial string stability).

7.10 Reflected brownian motion in a non convex cone

Participant: Guy Fayolle.

1. In 15, we prove that the stationary distribution can be found by solving a two dimensional vector boundary value problem (BVP) on a single curve (an hyperbola) for the associated Laplace transforms. The reduction to this kind of vector BVP seems to be quite new in the literature. As a matter of comparison, one single boundary condition is sufficient in the convex case. When the parameters of the model (drift, reflection angles and covariance matrix) are symmetric with respect to the bisector line of the cone, the model is reducible to a standard reflected Brownian motion in a convex cone. Finally, we construct a one-parameter family of distributions, which surprisingly provides, for any wedge (convex or not), one particular example of stationary distribution of a reflected Brownian motion.
2. In 16, the main result is to show that the stationary distribution can indeed be obtained by solving a boundary value problem of the same kind as the one encountered in the quarter plane, up to various dualities and symmetries. The idea is to start from Fourier (and not Laplace) transforms, allowing to get a functional equation for a single function of two complex variables.

7.11 Random walks in orthants and lattice path combinatorics

Participant: Guy Fayolle.

8.1 Bilateral contracts with industry

Participants: Fawzi Nashashibi, Anne Verroust-Blondet.

• Several CIFRE like PhD thesis are under construction between Valeo and Inria.

  Mr. Karim ESSALMI will join ASTRA in 2023 as a new PhD student working on Maneuver decision and Motion planning. Another PhD student will be working on Localization. A third student student will be working in the vision group within ASTRA.

• Othe PhD students and post-docs are jointly funded by Valeo and Inria while Mr. Nelson de Moura is hired as a 2-years post-doc thanks to the national Plan de relance Programme.
• Valeo is currently a major financing partner of the “GAT” international Chaire/JointLab in which Inria is a partner. The other partners are: UC Berkeley, Shanghai Jiao-Tong University, EPFL, IFSTTAR, Stellantis and SAFRAN.
• Technology transfer is also a major collaboration topic between ASTRA and Valeo as well as the development of a road automated prototype.
• Finally, Inria and Valeo are partners of the French project SAMBA (Sécurité Active et MoBilités Autonomes) including SAFRAN Group, Inria Paris, TwinswHeel, Soben, Stanley Robotics and EXPLEO.

9.1 International initiatives

9.2 National initiatives

10.1 Promoting scientific activities

10.2 Teaching - Supervision - Juries

10.3 Popularization

11.1 Major publications

• 1 inproceedingsZ.Zayed Alsayed, G.Guillaume Bresson, A.Anne Verroust-Blondet and F.Fawzi Nashashibi. 2D SLAM Correction Prediction in Large Scale Urban Environments.ICRA 2018 - International Conference on Robotics and Automation 2018Brisbane, AustraliaMay 2018
  HAL
• 2 bookG.Guy Fayolle, R.Roudolf Iasnogorodski and V. A.Vadim A. Malyshev. Random Walks in the Quarter Plane: Algebraic Methods, Boundary Value Problems, Applications to Queueing Systems and Analytic Combinatorics.40Probability Theory and Stochastic ModellingSpringer International PublishingFebruary 2017, 255
  HALDOIback to text
• 3 articleC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes, A.Alessandro Attanasi, M.Marco Pezzulla and G.Guido Gentile. Short-term Forecasting of Urban Traffic using Spatio-Temporal Markov Field.IEEE Transactions on Intelligent Transportation Systems2382022, 10858-10867
  HALDOIback to text
• 4 articleD.David González Bautista, J.Joshué Pérez, V.Vicente Milanés and F.Fawzi Nashashibi. A Review of Motion Planning Techniques for Automated Vehicles.IEEE Transactions on Intelligent Transportation SystemsApril 2016
  HALDOI
• 5 inproceedingsS. S.Shirsendu Sukanta Halder, J.-F.Jean-François Lalonde and R.Raoul de Charette. Physics-Based Rendering for Improving Robustness to Rain.ICCV 2019 - International Conference on Computer VisionSeoul, South KoreaOctober 2019
  HAL
• 6 articleM.Maximilian Jaritz, T.-H.Tuan-Hung Vu, R.Raoul de Charette, E.Emilie Wirbel and P.Patrick Pérez. Cross-Modal Learning for Domain Adaptation in 3D Semantic Segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence452March 2022, 1533-1544
  HALDOIback to textback to text
• 7 reportG.Gérard Le Lann. Cyberphysical Constructs and Concepts for Fully Automated Networked Vehicles.RR-9297INRIA Paris-RocquencourtOctober 2019
  HAL
• 8 articleI.Imane Mahtout, F.Francisco Navas, V.Vicente Milanés and F.Fawzi Nashashibi. Advances in Youla-Kucera parametrization: A Review.Annual Reviews in ControlJune 2020
  HALDOIback to text
• 9 articleK.Kaouther Messaoud, I.Itheri Yahiaoui, A.Anne Verroust-Blondet and F.Fawzi Nashashibi. Attention Based Vehicle Trajectory Prediction.IEEE Transactions on Intelligent Vehicles612021, 175-185
  HALDOIback to text
• 10 inproceedingsK.Kaouther Messaoud, I.Itheri Yahiaoui, A.Anne Verroust-Blondet and F.Fawzi Nashashibi. Non-local Social Pooling for Vehicle Trajectory Prediction.Intelligent Vehicles Symposium (IV)Paris, FranceJune 2019
  HALDOIback to text
• 11 inproceedingsF.Fabio Pizzati, P.Pietro Cerri and R.Raoul de Charette. CoMoGAN: continuous model-guided image-to-image translation.CVPR 2021 - IEEE Conference on Computer Vision and Pattern RecognitionIEEE Conference on Computer Vision and Pattern RecognitionOnline, FranceJune 2021
  HALback to textback to text
• 12 inproceedingsF.Fabio Pizzati, P.Pietro Cerri and R.Raoul de Charette. Model-based occlusion disentanglement for image-to-image translation.ECCV 2020 - European Conference on Computer VisionECCV 2020Glasgow / Virtual, United KingdomAugust 2020
  HALback to text
• 13 articleL.Luis Roldão, R.Raoul de Charette and A.Anne Verroust-Blondet. 3D Semantic Scene Completion: a Survey.International Journal of Computer Vision2021
  HALDOIback to textback to text
• 14 articleM.Maxime Tremblay, S. S.Shirsendu Sukanta Halder, R.Raoul de Charette and J.-F.Jean-François Lalonde. Rain Rendering for Evaluating and Improving Robustness to Bad Weather.International Journal of Computer VisionSeptember 2020
  HALDOIback to textback to text

11.2 Publications of the year

11.3 Cited publications

• 34 inproceedingsA.Alexander Amini, W.Wilko Schwarting, A.Ava Soleimany and D.Daniela Rus. Deep evidential regression.Advances in Neural Information Processing Systems (NeurIPS)2020
  back to text
• 35 miscI.I. Bae, J.Jaeyoun Moon, J.J. Jhung, H.H. Suk, T.T. Kim, H.H. Park, J.J. Cha, J.J. Kim, D.D. Kim and S.Shiho Kim. Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles.2020
  back to text
• 36 articleA.Arjun Balakrishnan, S. R.Sergio Rodriguez Florez and R.Roger Reynaud. Integrity Monitoring of Multimodal Perception System for Vehicle Localization.Sensors2020
  back to text
• 37 phdthesisP.Pierre de Beaucorps. Planification de trajectoire dans un environnement peu contraint et fortement dynamique.Sorbonne Université2019
  back to text
• 38 inproceedingsP.Pierre de Beaucorps, A.Anne Verroust-Blondet, R.Renaud Poncelet and F.Fawzi Nashashibi. RIS: A Framework for Motion Planning Among Highly Dynamic Obstacles.International Conference on Control, Automation, Robotics and Vision (ICARCV)2018
  back to text
• 39 inproceedingsJ.J. Behley, M.M. Garbade, A.A. Milioto, J.J. Quenzel, S.S. Behnke, C.C. Stachniss and J.J. Gall. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences.International Conference~on Computer Vision (ICCV)2019
  back to textback to text
• 40 inproceedingsV.Victor Besnier, H.Himalaya Jain, A.Andrei Bursuc, M.Matthieu Cord and P.Patrick Pérez. This dataset does not exist: training models from generated images.International Conference on Acoustics, Speech and Signal Processing (ICASSP)2020
  back to textback to text
• 41 inproceedingsM.Mario Bijelic, T.Tobias Gruber, F.Fahim Mannan, F.Florian Kraus, W.Werner Ritter, K.Klaus Dietmayer and F.Felix Heide. Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather.Conference on Computer Vision and Pattern Recognition (CVPR)2020
  back to textback to text
• 42 articleA.Alexandre Boulch. ConvPoint: Continuous convolutions for point cloud processing.Computers & Graphics2020
  back to textback to text
• 43 inproceedingsA.Alexandre Boulch, G.Gilles Puy and R.Renaud Marlet. FKAConv: Feature-Kernel Alignment for Point Cloud Convolution.Asian Conference on Computer Vision (ACCV)2020
  back to textback to text
• 44 articleK.Kyle Brown, K. R.Katherine Rose Driggs-Campbell and M. J.Mykel J. Kochenderfer. A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior.CoRR2020
  back to text
• 45 articleM.Maxime Bucher, T.-H.Tuan-Hung Vu, M.Matthieu Cord and P.Patrick Pérez. BUDA: Boundless Unsupervised Domain Adaptation in Semantic Segmentation.arXiv preprint arXiv:2004.011302020
  back to text
• 46 inproceedingsT.Thibault Buhet, E.Emilie Wirbel, A.Andrei Bursuc and X.Xavier Perrotton. PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving.Conference on Robot Learning (CoRL)2020
  back to text
• 47 miscT.Thibault Buhet, E.Emilie Wirbel and X.Xavier Perrotton. Conditional Vehicle Trajectories Prediction in CARLA Urban Environment.2019
  back to text
• 48 inproceedingsH.Holger Caesar, V.Varun Bankiti, A. H.Alex H. Lang, S.Sourabh Vora, V. E.Venice Erin Liong, Q.Qiang Xu, A.Anush Krishnan, Y.Yu Pan, G.Giancarlo Baldan and O.Oscar Beijbom. nuScenes: A Multimodal Dataset for Autonomous Driving.Conference on Computer Vision and Pattern Recognition (CVPR)2020
  back to textback to textback to text
• 49 articleA. Q.Anh Quan Cao, G.Gilles Puy, A.Alexandre Boulch and R.Renaud Marlet. PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds.Submitted for publication2021
  back to textback to text
• 50 inproceedingsM.-F.Ming-Fang Chang, J. W.John W Lambert, P.Patsorn Sangkloy, J.Jagjeet Singh, S.Slawomir Bak, A.Andrew Hartnett, D.De Wang, P.Peter Carr, S.Simon Lucey, D.Deva Ramanan and J.James Hays. Argoverse: 3D Tracking and Forecasting with Rich Maps.Conference on Computer Vision and Pattern Recognition (CVPR)2019
  back to text
• 51 articleD.Dongliang Chang, A.Aneeshan Sain, Z.Zhanyu Ma, Y.-Z.Yi-Zhe Song and J.Jun Guo. Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation.arXiv preprint arXiv:2003.037872020
  back to text
• 52 articleC.Chenyi Chen, A.Ari Seff, A. L.Alain L. Kornhauser and J.Jianxiong Xiao. DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving.CoRR2015
  back to text
• 53 inproceedingsY.Yunjey Choi, Y.Youngjung Uh, J.Jaejun Yoo and J.-W.Jung-Woo Ha. Stargan v2: Diverse image synthesis for multiple domains.Conference on Computer Vision and Pattern Recognition (CVPR)2020
  back to text
• 54 articleD.Derek Christie, A.Anne Koymans, T.Thierry Chanard, J.-M.Jean-Marc Lasgouttes and V.Vincent Kaufmann. Pioneering Driverless Electric Vehicles in Europe: The City Automated Transport System (CATS).Transportation Research Procedia13Towards future innovative transport: visions, trends and methods, 43rd European Transport Conference Selected Proceedings2016, 30--39URL: http://www.sciencedirect.com/science/article/pii/S2352146516300047
  DOIback to text
• 55 inproceedingsL.Laurène Claussmann, A.Ashwin Carvalho and G.Georg Schildbach. A path planner for autonomous driving on highways using a human mimicry approach with Binary Decision Diagrams.European Control Conference (ECC)2015
  back to text
• 56 inproceedingsL.Laurène Claussmann, M.Marie O'Brien, S.Sébastien Glaser, H.Homayoun Najjaran and D.Dominique Gruyer. Multi-Criteria Decision Making for Autonomous Vehicles using Fuzzy Dempster-Shafer Reasoning.Intelligent Vehicles Symposium (IV)2018
  back to text
• 57 inproceedingsJ.J. Colyar and J.J. Halkias. Us highway 101 dataset..Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT07-0302007
  back to text
• 58 inproceedingsJ.J. Colyar and J.J. Halkias. Us highway i-80 dataset..Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT-06-1372006
  back to text
• 59 incollectionC.Charles Corbière, N.Nicolas Thome, A.Avner Bar-Hen, M.Matthieu Cord and P.Patrick Pérez. Addressing Failure Prediction by Learning Model Confidence.Advances in Neural Information Processing Systems (NeurIPS)Curran Associates, Inc.2019
  back to textback to text
• 60 inproceedingsS.Shumo Cui, B.Benjamin Seibold, R.Raphael Stern and D. B.Daniel B. Work. Stabilizing traffic flow via a single autonomous vehicle: possibilities and limitations.Intelligent Vehicles Symposium (IV)2017
  back to text
• 61 articleR. F.Rafael F. Cunha, T. R.Tiago R. Gonçalves, V. S.Vineeth S. Varma, S. E.Salah E. Elayoubi and M.Ming Cao. Reducing fuel consumption in platooning systems through reinforcement learning.IFAC-PapersOnLine55156th IFAC Conference on Intelligent Control and Automation Sciences ICONS 20222022, 99-104URL: https://www.sciencedirect.com/science/article/pii/S2405896322010266
  DOIback to text
• 62 articleG.Guy Fayolle and J.-M.Jean-Marc Lasgouttes. Asymptotics and scalings for large closed product-form networks via the Central Limit Theorem.Markov Processes and Related Fields221996, 317-348
  back to text
• 63 phdthesisC.Carlos Flores. Control architecture for adaptive and cooperative car-following.Université Paris sciences et lettresDecember 2018
  HALback to text
• 64 inproceedingsC.Carlos Flores, V.Vicente Milanés and F.Fawzi Nashashibi. Using Fractional Calculus for Cooperative Car-Following Control.Intelligent Transportation Systems Conference 2016IEEERio de Janeiro, BrazilNovember 2016
  HALback to text
• 65 inproceedingsM.Markus Forster, R.Raphael Frank, M.Mario Gerla and T.Thomas Engel. A Cooperative Advanced Driver Assistance System to mitigate vehicular traffic shock waves.INFOCOM - Conference on Computer Communications2014
  back to textback to text
• 66 articleG.Gianni Franchi, A.Andrei Bursuc, E.Emanuel Aldea, S.Séverine Dubuisson and I.Isabelle Bloch. Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification.arXiv preprint arXiv:2012.028182020
  back to text
• 67 inproceedingsG.Gianni Franchi, A.Andrei Bursuc, E.Emanuel Aldea, S.Séverine Dubuisson and I.Isabelle Bloch. TRADI: Tracking deep neural network weight distributions.European Conference on Computer Vision (ECCV)2020
  back to textback to text
• 68 inproceedingsC.Cyril Furtlehner, Y.Yufei Han, J.-M.Jean-Marc Lasgouttes, V.Victorin Martin, F.Fabrice Marchal and F.Fabien Moutarde. Spatial and Temporal Analysis of Traffic States on Large Scale Networks.Intelligent Transportation Systems Conference (ITSC)2010
  back to text
• 69 inproceedingsC.Cyril Furtlehner and J.-M.Jean-Marc Lasgouttes. A queueing theory approach for a multi-speed exclusion process..Traffic and Granular Flow '07Springer2009, 129-138URL: http://hal.archives-ouvertes.fr/hal-00175628/en/
  back to text
• 70 techreportC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes, A.Alessandro Attanasi, L.Lorenzo Meschini and M.Marco Pezzulla. Spatio-temporal Probabilistic Short-term Forecasting on Urban Networks.INRIA2018
  back to text
• 71 articleC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes and A.Anne Auger. Learning Multiple Belief Propagation Fixed Points for Real Time Inference.Physica A: Statistical Mechanics and its Applications2010
  back to text
• 72 inproceedingsC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes and A.Arnaud de La Fortelle. A belief propagation approach to traffic prediction using probe vehicles.Intelligent Transportation Systems Conference (ITSC)2007
  back to text
• 73 articleC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes and M.Maxim Samsonov. One-dimensional Particle Processes with Acceleration/Braking Asymmetry.Journal of Statistical Physics1476June 2012, 1113-1144
  HALDOIback to text
• 74 inproceedingsC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes and M.Maxim Samsonov. The Fundamental Diagram on the Ring Geometry for Particle Processes with Acceleration/Braking Asymmetry.TGF'11 - Traffic and Granular FlowMoscowDecember 2011, URL: http://hal.inria.fr/hal-00646988
  back to text
• 75 phdthesisF. J.Fernando Jose Garrido Carpio. Two-staged local trajectory planning based on optimal pre-planned curves interpolation for human-like driving in urban areas.Université Paris sciences et lettres2018
  back to text
• 76 articleF.Fernando Garrido, L.Leonardo González, V.Vicente Milanés, J.Joshué Pérez and F.Fawzi Nashashibi. A Two-Stage Real-Time Path Planning : Application to the Overtaking Manuever.IEEE AccessJuly 2020
  HALDOIback to text
• 77 inproceedingsJ. C.J. Christian Gerdes and S. M.Sarah M. Thornton. Implementable Ethics for Autonomous Vehicles.Autonomes Fahren: Technische, rechtliche und gesellschaftliche AspekteBerlin, HeidelbergSpringer Berlin Heidelberg2015, 87--102URL: https://doi.org/10.1007/978-3-662-45854-9_5
  DOIback to text
• 78 inproceedingsV.Vittorio Giammarino, M.Maolong Lv, S.Simone Baldi, P.Paolo Frasca and M. L.Maria L. Delle Monache. On a weaker notion of ring stability for mixed traffic with human-driven and autonomous vehicles.Conference on Decision and Control (CDC)2019
  back to text
• 79 inproceedingsB.Benjamin Graham, M.Martin Engelcke and L.Laurens Van Der Maaten. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks.Conference on Computer Vision and Pattern Recognition (CVPR)2018
  back to text
• 80 inproceedingsT.Tianyu Gu and J. M.John M. Dolan. On-Road Motion Planning for Autonomous Vehicles.Intelligent Robotics and Applications - International Conference, ICIRALecture Notes in Computer ScienceSpringer2012
  back to text
• 81 inproceedingsM.Mohamed Hadded, J.-M.Jean-Marc Lasgouttes, F.Fawzi Nashashibi and I.Ilias Xydias. Platoon Route Optimization for Picking up Automated Vehicles in an Urban Network.21st IEEE International Conference on Intelligent Transportation Systems2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC)Maui, United StatesNovember 2018
  HALback to text
• 82 articleM.Mohamed Hadded, P.Pascale Minet and J.-M.Jean-Marc Lasgouttes. A game theory-based route planning approach for automated vehicle collection.Concurrency and Computation: Practice and ExperienceFebruary 2021
  HALDOIback to text
• 83 articleJ. A.Joelle Al Hage, P.Philippe Xu, P.Philippe Bonnifait and J.Javier Ibanez-Guzman. Localization Integrity for Intelligent Vehicles Through Fault Detection and Position Error Characterization.Transactions on Intelligent Transportation Systems (T-ITS)2020
  back to text
• 84 articleZ.Zhenliang He, W.Wangmeng Zuo, M.Meina Kan, S.Shiguang Shan and X.Xilin Chen. Attgan: Facial attribute editing by only changing what you want.Transactions on Image Processing2019
  back to text
• 85 inproceedingsS.Simon Hecker, D.Dengxin Dai and L.Luc Van Gool. Failure prediction for autonomous driving.Intelligent Vehicles Symposium (IV)2018
  back to text
• 86 inproceedingsI.Isabell Hofstetter, M.Michael Sprunk, F.Frank Schuster, F.Florian Ries and M.Martin Haueis. On Ambiguities in Feature-Based Vehicle Localization and their A Priori Detection in Maps.Intelligent Vehicles Symposium (IV)2019
  back to text
• 87 articleY.Yue Hu and D. B.Daniel B. Work. Robust Tensor Recovery with Fiber Outliers for Traffic Events.Trans. Knowl. Discov. Data2021
  back to text
• 88 miscS.SAE International. SAE Standards: J3016 automated-driving graphic..
  back to text
• 89 articleJ.Joel Janai, F.Fatma Güney, A.Aseem Behl and A.Andreas Geiger. Computer vision for autonomous vehicles: Problems, datasets and state of the art.Foundations and Trends in Computer Graphics and Vision2020
  back to text
• 90 inproceedingsM.Maximilian Jaritz, R.Raoul De Charette, E.Emilie Wirbel, X.Xavier Perrotton and F.Fawzi Nashashibi. Sparse and dense data with CNNs: Depth completion and semantic segmentation.International Conference on 3D Vision (3DV)2018
  back to text
• 91 inproceedingsM.Maximilian Jaritz, T.-H.Tuan-Hung Vu, R. d.Raoul de Charette, E.Emilie Wirbel and P.Patrick Pérez. xmuda: Cross-modal unsupervised domain adaptation for 3D semantic segmentation.Conference on Computer Vision and Pattern Recognition (CVPR)2020
  back to textback to textback to text
• 92 articleP.Prannay Khosla, P.Piotr Teterwak, C.Chen Wang, A.Aaron Sarna, Y.Yonglong Tian, P.Phillip Isola, A.Aaron Maschinot, C.Ce Liu and D.Dilip Krishnan. Supervised contrastive learning.arXiv preprint arXiv:2004.113622020
  back to text
• 93 inproceedingsR.Robert Krajewski, J. B.Julian Bock qnd Laurent Kloeker and L.Lutz Eckstein. The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems.Intelligent Transportation Systems Conference (ITSC)2018
  back to text
• 94 inproceedingsY.Yoshiaki Kuwata, G. A.Gaston A. Fiore, J.Justin Teo, E.Emilio Frazzoli and J. P.Jonathan P. How. Motion planning for urban driving using RRT.International Conference on Intelligent Robots and Systems (IROS)2008
  back to text
• 95 miscM. M.MIT Media Lab. Moral Machine - Human Perspectives on Machine Ethics.
  back to text
• 96 inproceedingsP.-A.Pierre-Alain Langlois, A.Alexandre Boulch and R.Renaud Marlet. Surface reconstruction from 3D line segments.International Conference on 3D Vision (3DV)2019
  back to text
• 97 inproceedingsJ.-M.Jean-Marc Lasgouttes. Global On-line Optimization for Charging Station Allocation.Intelligent Transportation Systems Conference, ITSC 2015IEEE2015
  back to text
• 98 inproceedingsJ.-M.Jean-Marc Lasgouttes, A.Ahmed Soua and O.Oyunchimeg Shagdar. Toward Efficient Simulation Platform for Platoon Communication in Large Scale C-ITS Scenarios.IEEE International Symposium on Networks, Computers and CommunicationsRoma, ItalyJune 2018
  HALback to text
• 99 inproceedingsO.Olivier Le Marchand, P.Philippe Bonnifait, J.Javier Ibañez-Guzmán and D.David Betaille. Automotive localization integrity using proprioceptive and pseudo-ranges measurements.Accurate Localization for Land TransportationLes Collections de l'INRETS2009
  back to text
• 100 inproceedingsO.Olivier Le Marchand, P.Philippe Bonnifait, J.Javier Ibañez-Guzmán and D.David Betaille. Vehicle Localization Integrity Based on Trajectory Monitoring.Intelligent Robots and Systems (IROS)2009
  back to text
• 101 inproceedingsG.Guillaume Le Moing, T.-H.Tuan-Hung Vu, H.Himalaya Jain, M.Mathieu Cord and P.Patrick Pérez. Semantic Palette: Guiding Scene Generation with Class Proportions.Conference on Computer Vision and Pattern Recognition (CVPR)2021
  back to text
• 102 inproceedingsD.-H.Dong-Hyun Lee. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.Workshop on challenges in representation learning (ICML)2013
  back to text
• 103 articleS.Stéphanie Lefèvre, D.Dizan Vasquez and C.Christian Laugier. A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH Journal112014, URL: http://www.robomechjournal.com/content/1/1/1
  DOIback to text
• 104 phdthesisE.Edouard Leurent. Safe and Efficient Reinforcement Learning for Behavioural Planning in Autonomous Driving.Université de Lille2020
  back to text
• 105 articleY.Yangyan Li, R.Rui Bu, M.Mingchao Sun, W.Wei Wu, X.Xinhan Di and B.Baoquan Chen. Pointcnn: Convolution on x-transformed points.Advances in Neural Information Processing Systems (NeurIPS)2018
  back to text
• 106 inproceedingsN.Nan Li, H.Hao Chen, I.Ilya Kolmanovsky and A.Anouck Girard. An Explicit Decision Tree Approach for Automated Driving.ASME 2017 Dynamic Systems and Control ConferenceDynamic Systems and Control Conference2017
  back to text
• 107 articleN. I.Nan I. Li, D. W.Dave W. Oyler, M.Mengxuan Zhang, Y.Yildiray Yildiz, I. V.Ilya V. Kolmanovsky and A. R.Anouck R. Girard. Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems.CoRR2016
  back to text
• 108 articleC.Chengyao Li and S. L.Steven L. Waslander. Visual Measurement Integrity Monitoring for UAV Localization.CoRR2019
  back to text
• 109 articleM.Maxim Likhachev and D.Dave Ferguson. Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles.International Journal of Robotics Research2009
  back to text
• 110 inproceedingsZ.Ziwei Liu, Z.Zhongqi Miao, X.Xingang Pan, X.Xiaohang Zhan, D.Dahua Lin, S. X.Stella X Yu and B.Boqing Gong. Open compound domain adaptation.Conference on Computer Vision and Pattern Recognition (CVPR)2020
  back to text
• 111 phdthesisI.Imane Mahtout. Youla-Kucera based multi-objective controllers : Application to autonomous vehicles.Université Paris sciences et lettresDecember 2020
  HALback to text
• 112 inproceedingsA.Andrey Malinin and M.Mark Gales. Predictive uncertainty estimation via prior networks.Advances in Neural Information Processing Systems (NeurIPS)2018
  back to text
• 113 inproceedingsV.Victorin Martin, C.Cyril Furtlehner, Y.Y. Han and J.-M.Jean-Marc Lasgouttes. GMRF Estimation under Topological and Spectral Constraints.ECML PKDD Proceedings,Part II2014
  back to text
• 114 articleV.Victorin Martin, J.-M.J.-M. Lasgouttes and C.C. Furtlehner. Latent binary MRF for online reconstruction of large scale systems.Annals of Mathematics and Artificial Intelligence2015
  back to text
• 115 phdthesisK.Kaouther Messaoud. Deep Learning based Trajectory Prediction for Autonomous Vehicles.Sorbonne UniversitéJune 2021
  back to text
• 116 articleK.Kaouther Messaoud, N.Nachiket Deo, M. M.Mohan M. Trivedi and F.Fawzi Nashashibi. Multi-Head Attention with Joint Agent-Map Representation for Trajectory Prediction in Autonomous Driving.CoRR2020
  back to text
• 117 inproceedingsK.Kaouther Messaoud, I.Itheri Yahiaoui, A.Anne Verroust-Blondet and F.Fawzi Nashashibi. Relational Recurrent Neural Networks For Vehicle Trajectory Prediction.Intelligent Transportation Systems Conference (ITSC)IEEE2019
  back to text
• 118 articleB.Bjoern Michele, A.Alexandre Boulch, G.Gilles Puy, R.Renaud Marlet and M.Maxime Bucher. Generative Zero-Shot Learning for Classification and Semantic Segmentation of 3D Point Clouds.Submitted for publication2021
  back to text
• 119 inproceedingsN.Nelson de Moura, R.Raja Chatila, K.Katherine Evans, S.Stéphane Chauvier and E.Ebru DOGAN. Ethical decision making for autonomous vehicles.IEEE Symposium on Intelligent VehicleLas Vegas (virtual), United StatesOctober 2020
  HALback to textback to textback to text
• 120 phdthesisF.Francisco Navas Matos. Stability analysis for controller switching in autonomous vehicles.Université Paris sciences et lettresNovember 2018
  HALback to text
• 121 articleF.Francisco Navas, V.Vicente Milanés, C.Carlos Flores and F.Fawzi Nashashibi. Multi-Model Adaptive Control for CACC Applications.IEEE Transactions on Intelligent Transportation Systems2222021, 1206-1216
  DOIback to text
• 122 inproceedingsJ.Julia Nilsson, J.Jonas Fredriksson and E.Erik Coelingh. Rule-Based Highway Maneuver Intention Recognition.International Conference on Intelligent Transportation Systems (ITSC)2015
  back to text
• 123 inproceedingsY.Yaniv Ovadia, E.Emily Fertig, J.Jie Ren, Z.Zachary Nado, D.David Sculley, S.Sebastian Nowozin, J. V.Joshua V Dillon, B.Balaji Lakshminarayanan and J.Jasper Snoek. Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift.Advances in Neural Information Processing Systems (NeurIPS)2019
  back to text
• 124 articleP.Plamen Petrov and F.Fawzi Nashashibi. Modeling and Nonlinear Adaptive Control for Autonomous Vehicle Overtaking.IEEE Transactions on Intelligent Transportation Systems154August 2014, 14
  HALDOIback to text
• 125 inproceedingsP.Plamen Petrov and F.Fawzi Nashashibi. Saturated Feedback Control for an Automated Parallel Parking Assist System.13th International Conference on Control, Automation, Robotics and Vision (ICARCV'14)Singapore, SingaporeDecember 2014
  HALback to text
• 126 articleM.Matthew Pitropov, D. E.Danson Evan Garcia, J.Jason Rebello, M.Michael Smart, C.Carlos Wang, K.Krzysztof Czarnecki and S.Steven Waslander. Canadian adverse driving conditions dataset.International Journal of Robotics Research2020
  back to text
• 127 inproceedingsF.Fabio Pizzati, R. d.Raoul de Charette, M.Michela Zaccaria and P.Pietro Cerri. Domain bridge for unpaired image-to-image translation and unsupervised domain adaptation.Winter Conference on Applications of Computer Vision (WACV)2020
  back to textback to textback to text
• 128 phdthesisF.Fabio Pizzati. Exploring domain-informed and physics-guided learning in image-to-image translation.Mines ParisNovembre 2022
  back to text
• 129 articleF.Fabio Pizzati, J.-F.Jean-François Lalonde and R.Raoul de Charette. ManiFest: Manifold Deformation for Few-shot Image Translation.arXiv2021
  back to text
• 130 inproceedingsG.Gilles Puy, A.Alexandre Boulch and R.Renaud Marlet. FLOT: Scene Flow on Point Clouds Guided by Optimal Transport.European Conference on Computer Vision (ECCV)2020
  back to textback to text
• 131 articleJ.Julien Rebut, A.Andrei Bursuc and P.Patrick Pérez. StyleLess layer: Improving robustness for real-world driving.arXiv preprint arXiv:2103.139052021
  back to textback to text
• 132 phdthesisL.Luis Roldao. 3D Scene Reconstruction and Completion for Autonomous Driving.Sorbonne UniversitéJuly 2021
  back to text
• 133 inproceedingsL.Luis Roldão, R.Raoul de Charette and A.Anne Verroust-Blondet. LMSCNet: Lightweight Multiscale 3D Semantic Completion.International Conference on 3D Vision (3DV)2020
  back to text
• 134 articleA.Andrey Rudenko, L.Luigi Palmieri, M.Michael Herman, K. M.Kris M Kitani, D. M.Dariu M Gavrila and K. O.Kai O Arras. Human motion trajectory prediction: a survey.The International Journal of Robotics Research2020
  back to text
• 135 articleC.Christos Sakaridis, D.Dengxin Dai and L.Luc Van Gool. ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding.arXiv preprint arXiv:2104.133952021
  back to text
• 136 inproceedingsM.Maxim Samsonov, C.Cyril Furtlehner and J.-M.Jean-Marc Lasgouttes. Exactly Solvable Stochastic Processes for Traffic Modelling.25th International Symposium on Computer and Information Sciences - ISCIS 2010Erol Gelenbe, Ricardo Lent, Georgia SakellariLondres, Royaume-UniSeptember 2010, 75-78URL: http://hal.inria.fr/inria-00533154
  back to text
• 137 inproceedingsS.Shuran Song, F.Fisher Yu, A.Andy Zeng, A. X.Angel X Chang, M.Manolis Savva and T.Thomas Funkhouser. Semantic scene completion from a single depth image.Conference on Computer Vision and Pattern Recognition (CVPR)2017
  back to text
• 138 inproceedingsA.Alexis Stoven-Dubois, K. K.Kuntima Kiala Miguel, A.Aziz Dziri, B.Bertrand Leroy and R.Roland Chapuis. A Collaborative Framework for High-Definition Mapping.2019 IEEE Intelligent Transportation Systems Conference (ITSC)2019, 1845-1850
  DOIback to text
• 139 inproceedingsP.Pei Sun, H.Henrik Kretzschmar, X.Xerxes Dotiwalla, A.Aurelien Chouard, V.Vijaysai Patnaik, P.Paul Tsui, J.James Guo, Y.Yin Zhou, Y.Yuning Chai, B.Benjamin Caine and others. Scalability in perception for autonomous driving: Waymo open dataset.Conference on Computer Vision and Pattern Recognition (CVPR)2020
  back to textback to text
• 140 inproceedingsH.Hugues Thomas, C. R.Charles R Qi, J.-E.Jean-Emmanuel Deschaud, B.Beatriz Marcotegui, F.François Goulette and L. J.Leonidas J Guibas. Kpconv: Flexible and deformable convolution for point clouds.International Conference on Computer Vision (ICCV)2019
  back to text
• 141 inproceedingsM.Marco Toldo, U.Umberto Michieli and P.Pietro Zanuttigh. Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings.Winter Conference on Applications of Computer Vision (WACV)2021
  back to text
• 142 inproceedingsT.-H.Tuan-Hung Vu, H.Himalaya Jain, M.Maxime Bucher, M.Matthieu Cord and P.Patrick Pérez. Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation.Conference on Computer Vision and Pattern Recognition (CVPR)2019
  back to text
• 143 inproceedingsT.-H.Tuan-Hung Vu, H.Himalaya Jain, M.Maxime Bucher, M.Matthieu Cord and P.Patrick Pérez. DADA: Depth-Aware Domain Adaptation in Semantic Segmentation.International Conference on Computer Vision (ICCV)2019
  back to text
• 144 articleP.Pin Wang, C.-Y.Ching-Yao Chan and A.Arnaud de La Fortelle. A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers.CoRR2018
  back to text
• 145 inproceedingsX.Xintao Wang, K.Ke Yu, C.Chao Dong, X.Xiaoou Tang and C. C.Chen Change Loy. Deep network interpolation for continuous imagery effect transition.Conference on Computer Vision and Pattern Recognition (CVPR)2019
  back to text
• 146 miscB.Brouk Wedajo and T.Thomas Heitzmann. Assistance á la conduite d'un véhicule automobile.May 2020
  back to text
• 147 inproceedingsC.Cathy Wu, A.Alexandre Bayen and A.Ankur Mehta. Stabilizing Traffic with Autonomous Vehicles.International Conference on Robotics and Automation (ICRA)2018
  back to text
• 148 inproceedingsY.Yang Xiao and R.Renaud Marlet. Few-shot object detection and viewpoint estimation for objects in the wild.European Conference on Computer Vision (ECCV)2020
  back to text
• 149 inproceedingsY.Yang Xiao, X.Xuchong Qiu, P.-A.Pierre-Alain Langlois, M.Mathieu Aubry and R.Renaud Marlet. Pose from shape: Deep pose estimation for arbitrary 3D objects.British Machine Vision Conference (BMVC)2019
  back to text
• 150 inproceedingsS.Senthil Yogamani, C.Ciarán Hughes, J.Jonathan Horgan, G.Ganesh Sistu, P.Padraig Varley, D.Derek O'Dea, M.Michal Uricár, S.Stefan Milz, M.Martin Simon, K.Karl Amende and others. Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving.International Conference on Computer Vision (ICCV)2019
  back to text
• 151 inproceedingsK.Kaichao You, M.Mingsheng Long, Z.Zhangjie Cao, J.Jianmin Wang and M. I.Michael I Jordan. Universal domain adaptation.Conference on Computer Vision and Pattern Recognition (CVPR)2019
  back to text
• 152 inproceedingsF.Fisher Yu, W.Wenqi Xian, Y.Yingying Chen, F.Fangchen Liu, M.Mike Liao, V.Vashisht Madhavan and T.Trevor Darrell. Bdd100k: A diverse driving video database with scalable annotation tooling.Conference on Computer Vision and Pattern Recognition (CVPR)2020
  back to text
• 153 articleW.Wei Zhan, L.Liting Sun, D.Di Wang, H.Haojie Shi, A.Aubrey Clausse, M.Maximilian Naumann, J.Julius Kümmerle, H.Hendrik Königshof, C.Christoph Stiller, A.Arnaud de La Fortelle and M.Masayoshi Tomizuka. INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps.arXiv:1910.03088 [cs, eess]2019
  back to text
• 154 articleH.Hang Zhao, J.Jiyang Gao, T.Tian Lan, C.Chen Sun, B.Benjamin Sapp, B.Balakrishnan Varadarajan, Y.Yue Shen, Y.Yi Shen, Y.Yuning Chai, C.Cordelia Schmid, C.Congcong Li and D.Dragomir Anguelov. TNT: Target-driveN Trajectory Prediction.CoRR2020
  back to text
• 155 articleJ.Julius Ziegler, P.Philipp Bender, M.Markus Schreiber, H.Henning Lategahn, T.Tobias Strau\ss, C.Christoph Stiller, T.Thao Dang, U.Uwe Franke, N.Nils Appenrodt, C. G.Christoph G. Keller, E.Eberhard Kaus, R.Ralf Herrtwich, C.Clemens Rabe, D.David Pfeiffer, F.Frank Lindner, F.Fridtjof Stein, F.Friedrich Erbs, M.Markus Enzweiler, C.Carsten Knöppel, J.Jochen Hipp, M.Marti Haueis, M.Maximilian Trepte, C.Carsten Brenk, A.Andreas Tamke, M.Mohammad Ghanaat, M.Markus Braun, A.Armin Joos, H.Hans Fritz, H.Horst Mock, M.Martin Hein and E.Eberhard Zeeb. Making Bertha Drive—An Autonomous Journey on a Historic Route.Intelligent Transportation Systems Magazine (ITS Magazine)2014
  back to text