mlsanityData to 14 Oct 2026

A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model

arXiv 2408.12805 ↗First posted 23 Aug 2024cs.AIcited by 1

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Abstract (arXiv)

Autonomous driving systems with self-evolution capabilities have the potential to independently evolve in complex and open environments, allowing to handle more unknown scenarios. However, as a result of the safety-performance trade-off mechanism of evolutionary algorithms, it is difficult to ensure safe exploration without sacrificing the improvement ability. This problem is especially prominent in dynamic traffic scenarios. Therefore, this paper proposes a safe self-evolution algorithm for autonomous driving based on data-driven risk quantification model. Specifically, a risk quantification model based on the attention mechanism is proposed by modeling the way humans perceive risks during driving, with the idea of achieving safety situation estimation of the surrounding environment through a data-driven approach. To prevent the impact of over-conservative safety guarding policies on the self-evolution capability of the algorithm, a safety-evolutionary decision-control integration algorithm with adjustable safety limits is proposed, and the proposed risk quantization model is integrated into it. Simulation and real-vehicle experiments results illustrate the effectiveness of the proposed method. The results show that the proposed algorithm can generate safe and reasonable actions in a variety of complex scenarios and guarantee safety without losing the evolutionary potential of learning-based autonomous driving systems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 53 references that are in the map. First posted on arXiv 23 Aug 2024.

Builds on

19 of 53 references.

References · 19

  1. 2022Planning-oriented Autonomous Drivingcited by 1,087
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7 more
  1. 2019Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraintscited by 24
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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Predictioncited by 4

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  1. 2024A Safety-Oriented Self-Learning Algorithm for Autonomous Driving: Evolution Starting from a Basic Model
  2. 2024A Safe and Efficient Self-evolving Algorithm for Decision-making and Control of Autonomous Driving Systems
  3. 2022How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies
  4. 2025Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection
  5. 2024SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for Autonomous Vehicles with Large Language Models
  6. 2024Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation
  7. 2025Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow
  8. 2021Don't Get Yourself into Trouble! Risk-aware Decision-Making for Autonomous Vehicles