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 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 …
Driver digital twin for online prediction of personalized lane-change behavior
Connected and automated vehicles (CAVs) are supposed to share the road with human-
driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic …
driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic …
Adaptive lane change trajectory planning scheme for autonomous vehicles under various road frictions and vehicle speeds
This paper proposes an adaptive lane change trajectory planning scheme to road friction
and vehicle speed for autonomous driving, while considering both the maneuver safety and …
and vehicle speed for autonomous driving, while considering both the maneuver safety and …
Human-like decision-making of autonomous vehicles in dynamic traffic scenarios
With the maturation of autonomous driving technology, the use of autonomous vehicles in a
socially acceptable manner has become a growing demand of the public. Human-like …
socially acceptable manner has become a growing demand of the public. Human-like …
Autonomous driving trajectory optimization with dual-loop iterative anchoring path smoothing and piecewise-jerk speed optimization
This letter presents a free space trajectory optimization algorithm for autonomous driving,
which decouples the collision-free trajectory generation problem into a Dual-Loop Iterative …
which decouples the collision-free trajectory generation problem into a Dual-Loop Iterative …
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 …
An efficient spatial-temporal trajectory planner for autonomous vehicles in unstructured environments
As a fundamental component of autonomous driving systems, motion planning has garnered
significant attention from both academia and industry. This paper focuses on efficient and …
significant attention from both academia and industry. This paper focuses on efficient and …
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 …
Meta-learning with less forgetting on large-scale non-stationary task distributions
The paradigm of machine intelligence moves from purely supervised learning to a more
practical scenario when many loosely related unlabeled data are available and labeled data …
practical scenario when many loosely related unlabeled data are available and labeled data …