Causal imitative model for autonomous driving
Imitation learning is a powerful approach for learning autonomous driving policy by
leveraging data from expert driver demonstrations. However, driving policies trained via …
leveraging data from expert driver demonstrations. However, driving policies trained via …
Imitation learning based decision-making for autonomous vehicle control at traffic roundabouts
The essential of developing an advanced driving assistance system is to learn human-like
decisions to enhance driving safety. When controlling a vehicle, joining roundabouts …
decisions to enhance driving safety. When controlling a vehicle, joining roundabouts …
Multi-task safe reinforcement learning for navigating intersections in dense traffic
Multi-task intersection navigation, which includes unprotected turning left, turning right, and
going straight in heavy traffic, remains a difficult task for autonomous vehicles. For the …
going straight in heavy traffic, remains a difficult task for autonomous vehicles. For the …
Oil: Observational imitation learning
Recent work has explored the problem of autonomous navigation by imitating a teacher and
learning an end-to-end policy, which directly predicts controls from raw images. However …
learning an end-to-end policy, which directly predicts controls from raw images. However …
Agen: Adaptable generative prediction networks for autonomous driving
In highly interactive driving scenarios, accurate prediction of other road participants is critical
for safe and efficient navigation of autonomous cars. Prediction is challenging due to the …
for safe and efficient navigation of autonomous cars. Prediction is challenging due to the …
Carl-lead: Lidar-based end-to-end autonomous driving with contrastive deep reinforcement learning
Autonomous driving in urban crowds at unregulated intersections is challenging, where
dynamic occlusions and uncertain behaviors of other vehicles should be carefully …
dynamic occlusions and uncertain behaviors of other vehicles should be carefully …
Deep reinforcement learning based vehicle navigation amongst pedestrians using a grid-based state representation
N Deshpande, A Spalanzani - 2019 IEEE Intelligent …, 2019 - ieeexplore.ieee.org
Autonomous navigation in structured urban environments amongst pedestrians is a
challenging and less explored problem. In this work, we propose to use a deep …
challenging and less explored problem. In this work, we propose to use a deep …
Cirl: Controllable imitative reinforcement learning for vision-based self-driving
Autonomous urban driving navigation with complex multi-agent dynamics is under-explored
due to the difficulty of learning an optimal driving policy. The traditional modular pipeline …
due to the difficulty of learning an optimal driving policy. The traditional modular pipeline …
Learning from Oracle demonstrations—a new approach to develop autonomous intersection management control algorithms based on multiagent deep reinforcement …
A Guillen-Perez, MD Cano - IEEE Access, 2022 - ieeexplore.ieee.org
Worldwide, many companies are working towards safe and innovative control systems for
Autonomous Vehicles (AVs). A key component is Autonomous Intersection Management …
Autonomous Vehicles (AVs). A key component is Autonomous Intersection Management …
Decision making for autonomous driving via augmented adversarial inverse reinforcement learning
Making decisions in complex driving environments is a challenging task for autonomous
agents. Imitation learning methods have great potentials for achieving such a goal …
agents. Imitation learning methods have great potentials for achieving such a goal …