A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
Interaction-aware decision-making for automated vehicles using social value orientation
Motion control algorithms in the presence of pedestrians are critical for the development of
safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on …
safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on …
Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends
The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the
safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully …
safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully …
Learning autonomous control policy for intersection navigation with pedestrian interaction
Z Zhu, H Zhao - IEEE Transactions on Intelligent Vehicles, 2023 - ieeexplore.ieee.org
In recent years, great efforts have been devoted to deep imitation learning for autonomous
driving control, where raw sensory inputs are directly mapped to control actions. However …
driving control, where raw sensory inputs are directly mapped to control actions. However …
Efficient statistical validation with edge cases to evaluate highly automated vehicles
D Karunakaran, S Worrall… - 2020 IEEE 23rd …, 2020 - ieeexplore.ieee.org
The widescale deployment of Autonomous Vehicles (AV) seems to be imminent despite
many safety challenges that are yet to be resolved. It is well known that there are no …
many safety challenges that are yet to be resolved. It is well known that there are no …
Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network
N Deshpande, D Vaufreydaz… - 2020 16th international …, 2020 - ieeexplore.ieee.org
Decision making for autonomous driving in urban environments is challenging due to the
complexity of the road structure and the uncertainty in the behavior of diverse road users …
complexity of the road structure and the uncertainty in the behavior of diverse road users …
Human-centric autonomous driving in an av-pedestrian interactive environment using svo
As Autonomous Vehicles (AV) are becoming a reality, the design of efficient motion control
algorithms will have to deal with the unpredictable and interactive nature of other road users …
algorithms will have to deal with the unpredictable and interactive nature of other road users …
Hierarchical framework integrating rapidly-exploring random tree with deep reinforcement learning for autonomous vehicle
This paper proposes a systematic driving framework where the decision making module of
reinforcement learning (RL) is integrated with rapidly-exploring random tree (RRT) as …
reinforcement learning (RL) is integrated with rapidly-exploring random tree (RRT) as …
Self-learned intelligence for integrated decision and control of automated vehicles at signalized intersections
Intersection is one of the most accident-prone urban scenarios for autonomous driving
wherein making safe and computationally efficient decisions is non-trivial. Current research …
wherein making safe and computationally efficient decisions is non-trivial. Current research …
Online trajectory planning with reinforcement learning for pedestrian avoidance
Planning the optimal trajectory of emergency avoidance maneuvers for highly automated
vehicles is a complex task with many challenges. The algorithm needs to decrease accident …
vehicles is a complex task with many challenges. The algorithm needs to decrease accident …