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End-to-end Autonomous Driving: Challenges and Frontiers

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arxiv 2306.16927 v3 pith:ZN3FVMBJ submitted 2023-06-29 cs.RO cs.AIcs.CVcs.LG

Li Chen , Penghao Wu , Kashyap Chitta , Bernhard Jaeger , Andreas Geiger , Hongyang Li This is my paper · ORCID
classification cs.ROcs.AIcs.CVcs.LG
keywords drivingend-to-endautonomouschallengesframeworkmodelsmotionactive
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 17 Pith papers

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    R2SE refines pretrained end-to-end driving policies on hard cases via residual LoRA reinforcement learning and switches between specialist and generalist policies using GPD-based uncertainty.

  4. Improving Traffic Signal Data Quality for the Waymo Open Motion Dataset

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    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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    Occ-LLM tokenizes 4D occupancy with a motion/static separation VAE and uses Llama-2 to forecast occupancy, plan ego motion, and answer scene questions, reporting state-of-the-art results on nuScenes.

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