12 accepted papers
High-definition (HD) map learning serves as an essential component of autonomous driving scene understanding, providing structured priors for planning and prediction. Recent transformer-based methods regress vectorized map elements via deformable attention over Bird's-Eye View (BEV) features. They t
High-definition (HD) map learning serves as an essential component of autonomous driving scene understanding, providing structured priors for planning and prediction. Recent transformer-based methods regress vectorized map elements via deformable attention over Bird’s-Eye View (BEV) features. They t…
Modeling expert driving behavior is crucial for the successful implementation of human-like autonomous driving. In this paper, we propose a new sampling-based Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) framework. It leverages naturalistic human driving data to train the reward mode
High-definition (HD) maps are crucial for autonomous driving systems. Despite recent advances in learning-based HD map prediction methods, these approaches experience significant performance degradation when encountering unseen weather or lighting conditions due to feature distribution discrepancies
High-definition (HD) maps play a crucial role in autonomous driving systems. Recent methods have attempted to construct HD maps in real-time using vehicle onboard sensors. Due to the inherent limitations of onboard sensors, which include sensitivity to detection range and susceptibility to occlusion…
Multi-Agent Path Finding (MAPF) is an important issue in multi-agent cooperation. Many studies apply MultiAgent Reinforcement Learning (MARL) to solve MAPF in partially observable settings. The objective of cooperative MARL is to maximize the cumulative team reward. Nevertheless, in partially observ…
Achieving fully autonomous driving in urban traffic scenarios is a significant challenge that necessitates balancing safety, efficiency, and compliance with traffic regulations. In this letter, we introduce a novel Curriculum Residual Hierarchical Reinforcement Learning (CR-HRL) framework. It integr
Velocity planning is an important module of autonomous driving, which aims to generate the velocity profile given a reference path. However, most existing algorithms fail to adequately address the uncertainty inherent in driving contexts, leading to potentially risky situations. To this end, we prop
Planning and prediction are two important modules of autonomous driving and have experienced tremendous advancement recently. Nevertheless, most existing methods regard planning and prediction as independent and ignore the correlation between them, leading to the lack of consideration for interactio…
Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from RGB images and rely on a time-consuming re-ranking step to exploit spatial struc…
Feature Pyramid Network (FPN) has been widely adopted to exploit multi-scale features for scale variation in object detection. However, intrinsic defects in most of the current methods with FPN make it difficult to adapt to the feature of different geometric objects. To address this issue, we introd…
Collision risk and smoothness are the most important factors in global path planning. Currently, planning methods that reduce global path collision risk and improve its smoothness through numerical optimization have achieved good results. However, these methods cannot always optimize the path. The r…