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End-to-end Autonomous Driving: Challenges and Frontiers
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The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion plans, instead of concentrating on individual tasks such as detection and motion prediction. End-to-end systems, in comparison to modular pipelines, benefit from joint feature optimization for perception and planning. This field has flourished due to the availability of large-scale datasets, closed-loop evaluation, and the increasing need for autonomous driving algorithms to perform effectively in challenging scenarios. In this survey, we provide a comprehensive analysis of more than 270 papers, covering the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving. We delve into several critical challenges, including multi-modality, interpretability, causal confusion, robustness, and world models, amongst others. Additionally, we discuss current advancements in foundation models and visual pre-training, as well as how to incorporate these techniques within the end-to-end driving framework. we maintain an active repository that contains up-to-date literature and open-source projects at https://github.com/OpenDriveLab/End-to-end-Autonomous-Driving.
Forward citations
Cited by 6 Pith papers
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Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
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Improving Traffic Signal Data Quality for the Waymo Open Motion Dataset
A trajectory-based, ring-and-barrier-constrained method imputes 71.7% of missing traffic signal states in the Waymo Open Motion Dataset and lowers the estimated red-light running rate from 15.7% to 2.9%.
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Ego-centric Learning of Communicative World Models for Autonomous Driving
Sharing compressed latent states and planned waypoints between agents, triggered by prediction errors, improves multi-agent driving performance in CARLA while cutting communication bandwidth by roughly 50x.
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AutoODD combines an LLM agent with per-axis Gaussian Process uncertainty to automatically discover failure modes of black-box models, demonstrated on missing-digit MNIST and aircraft detect-and-avoid.
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