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09/15/2023
MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly
We present MBAPPE, a novel approach to motion planning for autonomous dr...
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05/14/2023
TSGN: Temporal Scene Graph Neural Networks with Projected Vectorized Representation for Multi-Agent Motion Prediction
Predicting future motions of nearby agents is essential for an autonomou...
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03/13/2023
The Audio-Visual BatVision Dataset for Research on Sight and Sound
Vision research showed remarkable success in understanding our world, pr...
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05/15/2022
Uncertainty estimation for Cross-dataset performance in Trajectory prediction
While a lot of work has been done on developing trajectory prediction me...
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01/29/2022
Assessing Cross-dataset Generalization of Pedestrian Crossing Predictors
Pedestrian crossing prediction has been a topic of active research, resu...
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01/11/2022
Reward Relabelling for combined Reinforcement and Imitation Learning on sparse-reward tasks
During recent years, deep reinforcement learning (DRL) has made successf...
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11/16/2021
GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving
Deep reinforcement learning (DRL) has been demonstrated to be effective ...
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10/27/2021
Learning from demonstrations with SACR2: Soft Actor-Critic with Reward Relabeling
During recent years, deep reinforcement learning (DRL) has made successf...
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10/13/2021
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
In this paper, we propose THOMAS, a joint multi-agent trajectory predict...
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09/04/2021
GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation
In this paper, we propose GOHOME, a method leveraging graph representati...
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09/02/2021
TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction
Understanding the behaviors and intentions of pedestrians is still one o...
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05/23/2021
HOME: Heatmap Output for future Motion Estimation
In this paper, we propose HOME, a framework tackling the motion forecast...
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11/25/2019
End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances
Reinforcement Learning (RL) aims at learning an optimal behavior policy ...
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08/13/2019
Is Deep Reinforcement Learning Really Superhuman on Atari?
Consistent and reproducible evaluation of Deep Reinforcement Learning (D...
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11/30/2018
Multiview Based 3D Scene Understanding On Partial Point Sets
Deep learning within the context of point clouds has gained much researc...
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10/22/2018
Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning
This paper explores the capability of deep neural networks to capture ke...
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08/20/2018
End to End Vehicle Lateral Control Using a Single Fisheye Camera
Convolutional neural networks are commonly used to control the steering ...
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10/19/2010
Joint interpretation of on-board vision and static GPS cartography for determination of correct speed limit
We present here a first prototype of a "Speed Limit Support" Advance Dri...
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10/07/2009
Modular Traffic Sign Recognition applied to on-vehicle real-time visual detection of American and European speed limit signs
We present a new modular traffic signs recognition system, successfully ...
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10/07/2009
Visual object categorization with new keypoint-based adaBoost features
We present promising results for visual object categorization, obtained ...
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10/07/2009
Introducing New AdaBoost Features for Real-Time Vehicle Detection
This paper shows how to improve the real-time object detection in comple...
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10/07/2009
