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[HTML][HTML] Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control
Abstract Model-based reinforcement learning (RL) is anticipated to exhibit higher sample
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …
The integration of prediction and planning in deep learning automated driving systems: A review
Automated driving has the potential to revolutionize personal, public, and freight mobility.
Beside accurately perceiving the environment, automated vehicles must plan a safe …
Beside accurately perceiving the environment, automated vehicles must plan a safe …
Kinematics-aware multigraph attention network with residual learning for heterogeneous trajectory prediction
Trajectory prediction for heterogeneous traffic agents plays a crucial role in ensuring the
safety and efficiency of automated driving in highly interactive traffic environments …
safety and efficiency of automated driving in highly interactive traffic environments …
Interaction-aware trajectory planning for autonomous vehicles with analytic integration of neural networks into model predictive control
Autonomous vehicles (AVs) must share the driving space with other drivers and often
employ conservative motion planning strategies to ensure safety. These conservative …
employ conservative motion planning strategies to ensure safety. These conservative …
Ego‐planning‐guided multi‐graph convolutional network for heterogeneous agent trajectory prediction
Accurate prediction of the future trajectories of traffic agents is a critical aspect of
autonomous vehicle navigation. However, most existing approaches focus on predicting …
autonomous vehicle navigation. However, most existing approaches focus on predicting …
Reservoir computing for drone trajectory intent prediction: A physics informed approach
The design of accurate trajectory prediction algorithms is crucial to implement adequate
countermeasures against drones with anomalous performances. Wrong predictions may …
countermeasures against drones with anomalous performances. Wrong predictions may …
A hybrid deep reinforcement learning and optimal control architecture for autonomous highway driving
Autonomous vehicles in highway driving scenarios are expected to become a reality in the
next few years. Decision-making and motion planning algorithms, which allow autonomous …
next few years. Decision-making and motion planning algorithms, which allow autonomous …
Towards scalable & efficient interaction-aware planning in autonomous vehicles using knowledge distillation
Real-world driving involves intricate interactions among vehicles navigating through dense
traffic scenarios. Recent research focuses on enhancing the interaction awareness of …
traffic scenarios. Recent research focuses on enhancing the interaction awareness of …
Motion planning for autonomous driving with real traffic data validation
Accurate trajectory prediction of surrounding road users is the fundamental input for motion
planning, which enables safe autonomous driving on public roads. In this paper, a safe …
planning, which enables safe autonomous driving on public roads. In this paper, a safe …
Spatio-Temporal Corridor-Based Motion Planning of Lane Change Maneuver for Autonomous Driving in Multi-Vehicle Traffic
This paper presents a methodology of lane change motion planning based on spatio-
temporal corridor for autonomous driving in multi-vehicle traffic environments. The spatio …
temporal corridor for autonomous driving in multi-vehicle traffic environments. The spatio …