Lane change strategies for autonomous vehicles: A deep reinforcement learning approach based on transformer
End-to-end approaches are one of the most promising solutions for autonomous vehicles
(AVs) decision-making. However, the deployment of these technologies is usually …
(AVs) decision-making. However, the deployment of these technologies is usually …
Toward personalized decision making for autonomous vehicles: a constrained multi-objective reinforcement learning technique
Reinforcement learning promises to provide a state-of-the-art solution to the decision
making problem of autonomous driving. Nonetheless, numerous real-world decision making …
making problem of autonomous driving. Nonetheless, numerous real-world decision making …
Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects
Connected and automated vehicles (CAVs) have emerged as a potential solution to the
future challenges of developing safe, efficient, and eco-friendly transportation systems …
future challenges of developing safe, efficient, and eco-friendly transportation systems …
Robust multiagent reinforcement learning toward coordinated decision-making of automated vehicles
Automated driving is essential for developing and deploying intelligent transportation
systems. However, unavoidable sensor noises or perception errors may cause an …
systems. However, unavoidable sensor noises or perception errors may cause an …
A bi-level network-wide cooperative driving approach including deep reinforcement learning-based routing
Cooperative driving of connected and automated vehicles (CAVs) has attracted extensive
attention and researchers have proposed various approaches. However, existing …
attention and researchers have proposed various approaches. However, existing …
Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations
Despite the substantial advancements in reinforcement learning (RL) in recent years,
ensuring trustworthiness remains a formidable challenge when applying this technology to …
ensuring trustworthiness remains a formidable challenge when applying this technology to …
Dynamic testing for autonomous vehicles using random quasi monte carlo
The substantial resource usage required to create ample scenarios for testing Autonomous
Vehicles (AV) presents a bottleneck in their implementation. At present, research relies on …
Vehicles (AV) presents a bottleneck in their implementation. At present, research relies on …
CAVSim: A microscopic traffic simulator for evaluation of connected and automated vehicles
Connected and automated vehicles (CAVs) are expected to play a vital role in the emerging
intelligent transportation system. In recent years, researchers have proposed various …
intelligent transportation system. In recent years, researchers have proposed various …
LLM-based Operating Systems for Automated Vehicles: A New Perspective
The deployment of large language models (LLMs) brings challenges to intelligent systems
because its capability of integrating large-scale training data facilitates contextual reasoning …
because its capability of integrating large-scale training data facilitates contextual reasoning …
VistaScenario: Interaction Scenario Engineering for Vehicles with Intelligent Systems for Transport Automation
Intelligent vehicles and autonomous driving systems rely on scenario engineering for
intelligence and index (I&I), calibration and certification (C&C), and verification and …
intelligence and index (I&I), calibration and certification (C&C), and verification and …