7 papers
Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving
Jiazhuo Li, Linjiang Cao, Qi Liu +1
Sample-efficient reinforcement learning for autonomous driving is often limited by the trade-off between data efficiency and model bias. While world models reduce the reliance on c…
Kinematics-Aware Latent World Models for Data-Efficient Autonomous Driving
Jiazhuo Li, Linjiang Cao, Qi Liu +1
Data-efficient learning remains a central challenge in autonomous driving due to the high cost and safety risks of large-scale real-world interaction. Although world-model-based re…
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows
Linjiang Cao, Maonan Wang, Xi Xiong
The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a classic NP-hard combinatorial optimization problem widely applied in logistics distribution and transportati…
A Multi-Agent Rollout Approach for Highway Bottleneck Decongestion in Mixed Autonomy
Lu Liu, Maonan Wang, Man-On Pun +1
The integration of autonomous vehicles (AVs) into the existing transportation infrastructure offers a promising solution to alleviate congestion and enhance mobility. This research…
AdvSwap: Covert Adversarial Perturbation with High Frequency Info-swapping for Autonomous Driving Perception
Yuanhao Huang, Qinfan Zhang, Jiandong Xing +4
Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby com…
iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement
Aoyu Pang, Maonan Wang, Man-On Pun +2
Urban congestion remains a critical challenge, with traffic signal control (TSC) emerging as a potent solution. TSC is often modeled as a Markov Decision Process problem and then s…