Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline
Current end-to-end autonomous driving methods either run a controller based on a planned
trajectory or perform control prediction directly, which have spanned two separately studied …
trajectory or perform control prediction directly, which have spanned two separately studied …
Hidden biases of end-to-end driving models
End-to-end driving systems have recently made rapid progress, in particular on CARLA.
Independent of their major contribution, they introduce changes to minor system …
Independent of their major contribution, they introduce changes to minor system …
End-to-End Autonomous Driving in CARLA: A Survey
Autonomous Driving (AD) has evolved significantly since its beginnings in the 1980s, with
continuous advancements driven by both industry and academia. Traditional AD systems …
continuous advancements driven by both industry and academia. Traditional AD systems …
Drivecot: Integrating chain-of-thought reasoning with end-to-end driving
End-to-end driving has made significant progress in recent years, demonstrating benefits
such as system simplicity and competitive driving performance under both open-loop and …
such as system simplicity and competitive driving performance under both open-loop and …
LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learning
In end-to-end autonomous driving, the utilization of existing sensor fusion techniques for
imitation learning proves inadequate in challenging situations that involve numerous …
imitation learning proves inadequate in challenging situations that involve numerous …
Good Data Is All Imitation Learning Needs
In this paper, we address the limitations of traditional teacher-student models, imitation
learning, and behaviour cloning in the context of Autonomous/Automated Driving Systems …
learning, and behaviour cloning in the context of Autonomous/Automated Driving Systems …
Hidden Biases of End-to-End Driving Datasets
End-to-end driving systems have made rapid progress, but have so far not been applied to
the challenging new CARLA Leaderboard 2.0. Further, while there is a large body of …
the challenging new CARLA Leaderboard 2.0. Further, while there is a large body of …
Target-point Attention Transformer: A novel trajectory predict network for end-to-end autonomous driving
J Du, Y Zhao, H Cheng - arxiv preprint arxiv:2308.01496, 2023 - arxiv.org
In the field of autonomous driving, there have been many excellent perception models for
object detection, semantic segmentation, and other tasks, but how can we effectively use the …
object detection, semantic segmentation, and other tasks, but how can we effectively use the …
[BOOK][B] Distributed reinforcement learning for autonomous driving
Z Huang - 2022 - search.proquest.com
Due to the complex and safety-critical nature of autonomous driving, recent works typically
test their ideas on simulators designed for the very purpose of advancing self-driving …
test their ideas on simulators designed for the very purpose of advancing self-driving …
[PDF][PDF] Supplementary Materials: Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
A Dataset - proceedings.neurips.cc
In this Supplementary document, we first provide a detailed description of the dataset in Sec.
A. Implementation and training details are in Sec. B. We show detailed infraction statistics for …
A. Implementation and training details are in Sec. B. We show detailed infraction statistics for …