Enhancing Autonomous Driving Safety with Collision Scenario Integration

• URL: http://arxiv.org/abs/2503.03957v1
• Date: Wed, 05 Mar 2025 23:08:43 GMT
• Title: Enhancing Autonomous Driving Safety with Collision Scenario Integration
• Authors: Zi Wang, Shiyi Lan, Xinglong Sun, Nadine Chang, Zhenxin Li, Zhiding Yu, Jose M. Alvarez,
• Abstract summary: We propose SafeFusion, a training framework to learn from collision data.<n>Instead of over-relying on imitation learning, SafeFusion integrates safety-oriented metrics during training to enable collision avoidance learning.<n>We also propose CollisionGen, a scalable data generation pipeline to generate diverse, high-quality scenarios.
• Score: 36.83682052117178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous vehicle safety is crucial for the successful deployment of self-driving cars. However, most existing planning methods rely heavily on imitation learning, which limits their ability to leverage collision data effectively. Moreover, collecting collision or near-collision data is inherently challenging, as it involves risks and raises ethical and practical concerns. In this paper, we propose SafeFusion, a training framework to learn from collision data. Instead of over-relying on imitation learning, SafeFusion integrates safety-oriented metrics during training to enable collision avoidance learning. In addition, to address the scarcity of collision data, we propose CollisionGen, a scalable data generation pipeline to generate diverse, high-quality scenarios using natural language prompts, generative models, and rule-based filtering. Experimental results show that our approach improves planning performance in collision-prone scenarios by 56\% over previous state-of-the-art planners while maintaining effectiveness in regular driving situations. Our work provides a scalable and effective solution for advancing the safety of autonomous driving systems.

Related papers

• Foundation Models for Rapid Autonomy Validation [4.417336418010182]
  A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it could encounter. We propose the use of a behavior foundation model, specifically a masked autoencoder (MAE), trained to reconstruct driving scenarios.
  arXiv  Detail & Related papers  (2024-10-22T15:32:43Z)
• ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable [88.08120417169971]
  Machine learning based autonomous driving systems often face challenges with safety-critical scenarios that are rare in real-world data. This work explores generating safety-critical driving scenarios by modifying complex real-world regular scenarios through trajectory optimization. Our approach addresses unrealistic diverging trajectories and unavoidable collision scenarios that are not useful for training robust planner.
  arXiv  Detail & Related papers  (2024-09-12T08:26:33Z)
• A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts [17.434924472015812]
  This paper introduces a learning-based algorithm tailored to foster safe and efficient driving behaviors in roundabouts. The proposed algorithm employs a deep Q-learning network to learn safe and efficient driving strategies in complex multi-vehicle roundabouts. The results show that our proposed system consistently achieves safe and efficient driving whilst maintaining a stable training process.
  arXiv  Detail & Related papers  (2024-08-15T16:10:25Z)
• Collision Probability Distribution Estimation via Temporal Difference Learning [0.46085106405479537]
  We introduce CollisionPro, a pioneering framework designed to estimate cumulative collision probability distributions. We formulate our framework within the context of reinforcement learning to pave the way for safety-aware agents. A comprehensive examination of our framework is conducted using a realistic autonomous driving simulator.
  arXiv  Detail & Related papers  (2024-07-29T13:32:42Z)
• RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes [57.319845580050924]
  We propose a reinforcement learning framework that combines risk-sensitive control with an adaptive action space curriculum. We show that our algorithm is capable of learning high-speed policies for a real-world off-road driving task.
  arXiv  Detail & Related papers  (2024-05-07T23:32:36Z)
• Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving [33.672722472758636]
  offline Reinforcement Learning(RL) approaches exhibit notable efficacy in addressing sequential decision-making problems from offline datasets. We introduce the saFety-aware strUctured Scenario representatION ( Fusion) to facilitate the learning of a generalizable end-to-end driving policy. Empirical evidence in various driving scenarios attests that Fusion significantly enhances the safety and generalizability of autonomous driving agents.
  arXiv  Detail & Related papers  (2023-10-31T18:21:24Z)
• CAT: Closed-loop Adversarial Training for Safe End-to-End Driving [54.60865656161679]
  Adversarial Training (CAT) is a framework for safe end-to-end driving in autonomous vehicles. Cat aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios. Cat can effectively generate adversarial scenarios countering the agent being trained.
  arXiv  Detail & Related papers  (2023-10-19T02:49:31Z)
• Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios [147.16925581385576]
  We show how imitation learning combined with reinforcement learning can substantially improve the safety and reliability of driving policies. We train a policy on over 100k miles of urban driving data, and measure its effectiveness in test scenarios grouped by different levels of collision likelihood.
  arXiv  Detail & Related papers  (2022-12-21T23:59:33Z)
• Learning Interactive Driving Policies via Data-driven Simulation [125.97811179463542]
  Data-driven simulators promise high data-efficiency for driving policy learning. Small underlying datasets often lack interesting and challenging edge cases for learning interactive driving. We propose a simulation method that uses in-painted ado vehicles for learning robust driving policies.
  arXiv  Detail & Related papers  (2021-11-23T20:14:02Z)
• Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios [8.484564880157148]
  This paper presents a novel framework for interaction-aware motion planning in dense traffic scenarios. We propose to learn, via deep Reinforcement Learning (RL), an interaction-aware policy providing global guidance about the cooperativeness of other vehicles. The learned policy can reason and guide the local optimization-based planner with interactive behavior to pro-actively merge in dense traffic while remaining safe in case the other vehicles do not yield.
  arXiv  Detail & Related papers  (2021-07-09T16:43:12Z)
• Deep Structured Reactive Planning [94.92994828905984]
  We propose a novel data-driven, reactive planning objective for self-driving vehicles. We show that our model outperforms a non-reactive variant in successfully completing highly complex maneuvers.
  arXiv  Detail & Related papers  (2021-01-18T01:43:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.