While IL has been successfully applied in RL-based approaches for autonomous driving, significant challenges, such as limited data for RL and poor generalization in IL, still need further investigation. To overcome these limitations, we propose in this paper a novel approach that effectively combines IL with DRL by incorporating expert demonstration data to control AV in roundabout and right-turn intersection scenarios. Instead of employing CNNs, we integrate a ViT into the perception module of the SAC algorithm to extract key features from environmental images. The ViT algorithm excels in identifying relationships across different parts of an image, thereby enhancing environmental understanding, which leads to more accurate and precise decision making. Consequently, our approach not only boosts the performance of the DRL model but also accelerates its convergence, improving the overall efficiency and effectiveness of AVs in roundabouts and right-turn intersections with dense traffic by a achieving high success rate and low collision compared to RL baseline algorithms.
Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore, it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most of the related studies have focused on considering explicit collision risks while lackin…
Driving at an unsignalized roundabout is a complex traffic scenario that requires both traffic safety and efficiency. At the unsignalized roundabout, the driving policy does not simply maintain a safe distance for all vehicles. Instead, it pays more attention to vehicles that pot…
Autonomous vehicle (AV) navigation in dynamic urban environments faces challenges such as unpredictable traffic conditions, varying road user behaviors, and complex road networks. This study proposes a novel reinforcement learning-based framework that enhances AV decision making…
The application of deep learning in autonomous vehicles has surged over the years with advancements in technology. This research explores the integration of deep learning algorithms into autonomous vehicles (AVs), focusing on their role in perception, decision-making, localizatio…
This study presents the first investigation into the problem of autonomous vehicle (AV) merging into existing platoons, proposing a multi-agent deep reinforcement learning (MA-DRL)-based cooperative control framework. The developed MA-DRL architecture enables coordinated learning…
An intelligent, accurate, and powerful object detection system is required for automated driving systems to keep these vehicles aware of their surrounding objects. Thus, vehicles adapt their speed and operations to avoid crashing with the existing objects and follow the driving r…