[HTML][HTML] A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving
Abstract Recent advances in Intelligent Transport Systems (ITS) and Artificial Intelligence
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
Model-based control and model-free control techniques for autonomous vehicles: A technical survey
H Rizk, A Chaibet, A Kribèche - Applied Sciences, 2023 - mdpi.com
Autonomous driving has the potential to revolutionize mobility and transportation by
reducing road accidents, alleviating traffic congestion, and mitigating air pollution. This …
reducing road accidents, alleviating traffic congestion, and mitigating air pollution. This …
Reinforcement learning for autonomous driving with latent state inference and spatial-temporal relationships
Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …
complex autonomous driving scenarios. However, identifying the subtle cues that can …
Interaction-Aware Decision-Making for Autonomous Vehicles
Complex, dynamic, and interactive environment brings huge challenges to autonomous
driving technologies. Because of the strong interactions between different traffic participants …
driving technologies. Because of the strong interactions between different traffic participants …
DQ-GAT: Towards safe and efficient autonomous driving with deep Q-learning and graph attention networks
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …
Learning interaction-aware guidance policies for motion planning in dense traffic scenarios
B Brito, A Agarwal, J Alonso-Mora - arXiv preprint arXiv:2107.04538, 2021 - arxiv.org
Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
Spatiotemporal costmap inference for MPC via deep inverse reinforcement learning
It can be difficult to autonomously produce driver behavior so that it appears natural to other
traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this …
traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this …
Learning interaction-aware guidance for trajectory optimization in dense traffic scenarios
B Brito, A Agarwal… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
Runtime safety assurance using reinforcement learning
C Lazarus, JG Lopez… - 2020 AIAA/IEEE 39th …, 2020 - ieeexplore.ieee.org
The airworthiness and safety of a non-pedigreed autopilot must be verified, but the cost to
formally do so can be prohibitive. We can bypass formal verification of non-pedigreed …
formally do so can be prohibitive. We can bypass formal verification of non-pedigreed …
Interaction-aware model predictive control for autonomous driving
R Wang, M Schuurmans… - 2023 European Control …, 2023 - ieeexplore.ieee.org
We propose an interaction-aware stochastic model predictive control (MPC) strategy for lane
merging tasks in automated driving. The MPC strategy is integrated with an online learning …
merging tasks in automated driving. The MPC strategy is integrated with an online learning …