Robust decision making for autonomous vehicles at highway on-ramps: A constrained adversarial reinforcement learning approach
Reinforcement learning has demonstrated its potential in a series of challenging domains.
However, many real-world decision making tasks involve unpredictable environmental …
However, many real-world decision making tasks involve unpredictable environmental …
Toward trustworthy decision-making for autonomous vehicles: A robust reinforcement learning approach with safety guarantees
While autonomous vehicles are vital components of intelligent transportation systems,
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
Trajectory planning for autonomous vehicles using hierarchical reinforcement learning
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
Safe-state enhancement method for autonomous driving via direct hierarchical reinforcement learning
Reinforcement learning (RL) has shown excellent performance in the sequential decision-
making problem, where safety in the form of state constraints is of great significance in the …
making problem, where safety in the form of state constraints is of great significance in the …
Integrated eco-driving automation of intelligent vehicles in multi-lane scenario via model-accelerated reinforcement learning
The development of intelligent driving technologies is expected to have the potential in
energy economics. Some reported studies mainly focused on the economical driving …
energy economics. Some reported studies mainly focused on the economical driving …
A safe hierarchical planning framework for complex driving scenarios based on reinforcement learning
Autonomous vehicles need to handle various traffic conditions and make safe and efficient
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …
DRL-GAT-SA: Deep reinforcement learning for autonomous driving planning based on graph attention networks and simplex architecture
Y Peng, G Tan, H Si, J Li - Journal of Systems Architecture, 2022 - Elsevier
Self-driving cars need to make decisions while sharing the road with human drivers whose
behavior is uncertain. However, the presence of uncertainty leads to a trade-off between two …
behavior is uncertain. However, the presence of uncertainty leads to a trade-off between two …
Lexicographic actor-critic deep reinforcement learning for urban autonomous driving
Urban autonomous driving is a difficult task because of its complex road scenarios and the
interaction between multiple vehicles. Autonomous vehicles need to balance multiple …
interaction between multiple vehicles. Autonomous vehicles need to balance multiple …
A controllable agent by subgoals in path planning using goal-conditioned reinforcement learning
The aim of path planning is to search for a path from the starting point to the goal. Numerous
studies, however, have dealt with a single predefined goal. That is, an agent who has …
studies, however, have dealt with a single predefined goal. That is, an agent who has …
Research on path planning and path tracking control of autonomous vehicles based on improved APF and SMC
Y Zhang, K Liu, F Gao, F Zhao - Sensors, 2023 - mdpi.com
Path planning and tracking control is an essential part of autonomous vehicle research. In
terms of path planning, the artificial potential field (APF) algorithm has attracted much …
terms of path planning, the artificial potential field (APF) algorithm has attracted much …