用动态贝叶斯网络提升高速变道安全性，减少碰撞风险。
Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks
- 构建三重概率假设框架，融合横向与纵向安全评估
- 高精度预测变道行为，高速场景下碰撞率显著降低
- 适合自动驾驶系统安全验证，尤其适用于复杂交通场景
高速路段的切入变道行为易引发急刹和碰撞，亟需安全高效的变道策略。本文提出一种动态贝叶斯网络（DBN）框架，整合横向证据与安全评估模型，实现对变道行为的有效预测并保障切入安全。该框架包含三个核心概率假设：横向证据、横向安全与纵向安全，通过动态处理车辆位置、横向速度、相对距离及碰撞时间（TTC）等数据完成决策。实验表明，相较于传统方法，该模型在高速关键场景中表现出更优的碰撞抑制能力，同时在低速场景下保持良好性能，为自动驾驶系统的鲁棒性、可扩展性和高效安全验证提供了新路径。
原文摘要 · Abstract (English)
Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models, thereby predicting lane changes and ensuring safe cut-in maneuvers effectively. Our proposed framework comprises three key probabilistic hypotheses (lateral evidence, lateral safety, and longitudinal safety) that facilitate the decision-making process through dynamic data processing and assessments of vehicle positions, lateral velocities, relative distance, and Time-to-Collision (TTC) computations. The DBN model's performance compared with other conventional approaches demonstrates superior performance in crash reduction, especially in critical high-speed scenarios, while maintaining a competitive performance in low-speed scenarios. This paves the way for robust, scalable, and efficient safety validation in automated driving systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。