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End-to-end Autonomous Driving: Challenges and Frontiers (2306.16927v3)

Published 29 Jun 2023 in cs.RO, cs.AI, cs.CV, and cs.LG

Abstract: 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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End-to-end Autonomous Driving: Challenges and Frontiers

The paper "End-to-end Autonomous Driving: Challenges and Frontiers" presents a comprehensive analysis of the emerging domain of end-to-end autonomous driving systems. These systems integrate raw sensor inputs to generate driving actions, contrasting with traditional modular architectures that separately handle perception, prediction, and planning tasks. This survey explores methodologies, challenges, and potential developments in designing these complex systems by reviewing over 250 related papers.

Key Methodologies

The authors focus on categorizing end-to-end driving approaches into two primary learning paradigms: imitation learning (IL) and reinforcement learning (RL). IL methods, such as behavior cloning, model the driving policy based on expert demonstrations. Despite its simplicity, IL faces issues like covariate shift and causal confusion. On the other hand, RL techniques, though data-intensive, offer the flexibility of learning through interaction with the environment. Interestingly, hybrid models that combine IL with RL are emerging, leveraging prior knowledge to expedite policy refinement.

Benchmarks and Evaluation

The paper emphasizes the significance of benchmarking through both simulation (closed-loop) and real-world datasets (open-loop). Simulators offer controlled environments for testing diverse scenarios but may not capture real-world variability. The use of rich datasets from sources like nuScenes and Waymo supplements this by providing real-world driving data for evaluating system robustness.

Challenges

The paper provides an in-depth discussion on several critical challenges:

  • Multi-modal Fusion: Integrating data from heterogeneous sensors like cameras and LiDARs remains complex. Effective feature extraction across modalities is necessary for improving planning accuracy.
  • Robustness and Generalization: Issues like domain adaptation and covariate shift are pivotal. Systems must be trained to generalize across different driving environments, weather conditions, and geographic regions.
  • Causal Confusion: Challenges arise when models rely on spurious correlations, such as past vehicle speed, leading to incorrect predictions. Addressing this requires innovative architectural solutions.
  • Interpretability: There’s a need for enhancing the transparency of these systems, particularly through attention mechanisms, auxiliary tasks, and cost learning to provide insight into decision-making processes.

Implications and Future Developments

The paper outlines potential advancements that hold promise for the future of autonomous driving:

  • Large Foundation Models: Inspiration can be drawn from developments in large-scale LLMs to improve the capabilities of driving models.
  • Data-driven Simulation and Synthesis: Enhancing the realism and diversity of simulation environments will be crucial for training robust systems.
  • Zero-shot and Few-shot Learning: This encourages models to adapt to unseen scenarios with limited data, crucial for real-world deployment.
  • Vehicle-to-Everything (V2X) Communication: Incorporating V2X data can enhance situational awareness and decision-making, especially in complex traffic scenarios.

Conclusion

The paper emphasizes that while significant strides have been made in end-to-end autonomous driving, many challenges remain. Collaborative research efforts are needed to address these challenges through innovative algorithms, high-quality datasets, and comprehensive simulation environments. This survey acts as a guiding document for researchers looking to further the field of autonomous driving systems, paving the way for advancements toward safer and more reliable self-driving vehicles.

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Authors (6)
  1. Li Chen (590 papers)
  2. Penghao Wu (17 papers)
  3. Kashyap Chitta (30 papers)
  4. Bernhard Jaeger (7 papers)
  5. Andreas Geiger (136 papers)
  6. Hongyang Li (99 papers)
Citations (160)
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GitHub

  1. GitHub - OpenDriveLab/End-to-end-Autonomous-Driving: All you need for End-to-end Autonomous Driving (2,332 stars)