Data-centric evolution in autonomous driving: A comprehensive survey of big data system, data mining, and closed-loop technologies
The aspiration of the next generation's autonomous driving (AD) technology relies on the
dedicated integration and interaction among intelligent perception, prediction, planning, and …
dedicated integration and interaction among intelligent perception, prediction, planning, and …
Identify, estimate and bound the uncertainty of reinforcement learning for autonomous driving
Deep reinforcement learning (DRL) has emerged as a promising approach for developing
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …
Hi-SCL: Fighting long-tailed challenges in trajectory prediction with hierarchical wave-semantic contrastive learning
Predicting the future trajectories of traffic agents is a pivotal aspect in achieving collision-free
driving for autonomous vehicles. Although the overall accuracy of existing prediction …
driving for autonomous vehicles. Although the overall accuracy of existing prediction …
Autonomous Driving via Knowledge-Enhanced Safe Reinforcement Learning
Recently, the autonomous driving technology is at a critical phase evolving from typical,
closed scenarios to largescale, open driving scenarios, which is challenged by the diversity …
closed scenarios to largescale, open driving scenarios, which is challenged by the diversity …
Act Better by Timing: A timing-Aware Reinforcement Learning for Autonomous Driving
G Li, J Wu, Y He - arXiv preprint arXiv:2406.13223, 2024 - arxiv.org
Coping with intensively interactive scenarios is one of the significant challenges in the
development of autonomous driving. Reinforcement learning (RL) offers an ideal solution for …
development of autonomous driving. Reinforcement learning (RL) offers an ideal solution for …
CommonRoad-CARLA Interface: Bridging the Gap between MotionPlanning and 3D Simulation
S Maierhofer, M Althoff - 2024 IEEE Intelligent Vehicles …, 2024 - mediatum.ub.tum.de
Motion planning algorithms should be tested on a large, diverse, and realistic set of
scenarios before deploying them in real vehicles. However, existing 3D simulators usually …
scenarios before deploying them in real vehicles. However, existing 3D simulators usually …
Graph structure-based implicit risk reasoning for Long-tail scenarios of automated driving
With the development of Artificial Intelligence (AI) technology, autonomous vehicles (AVs)
have entered the general public's view, however, the challenges brought by" long-tail" …
have entered the general public's view, however, the challenges brought by" long-tail" …