路上行走对行人互动行为的影响:一种基于Markov游戏的多代理分析
Elena Abu Khuzam1, Gabriel Lanzaro1, Tarek Sayed1
1Department of Civil Engineering, University of British Columbia.
Accident; analysis and prevention
|June 15, 2025
概括
杰伊步行者表现出不可预测的动作,增加了碰撞风险. 这项研究使用多代理对抗逆强化学习 (MAAIRL) 模拟路径行走行为,以改善交通安全模拟.
科学领域:
- 交通安全 交通安全
- 行为建模行为建模
- 人工智能的人工智能
背景情况:
- 在繁忙的交通环境中,路人行走是一个重大的安全问题.
- 现有的行人模型往往无法解释道路行走行为及其对碰撞风险的影响.
- 司机面临着意想不到的互动,并必须采取回避行动,因为 jaywalkers.
研究的目的:
- 在使用多代理对抗逆强化学习 (MAAIRL) 的信号交叉点上模拟道路使用者的行为.
- 代表行人和司机在通行障碍情况下的动态决策策略.
- 开发奖励功能和最佳政策,以改善微模拟模型.
主要方法:
- 使用马科夫游戏框架与MAAIRL分析行人与驾驶员的互动.
- 获得的奖励函数用于推断行为和决策的最佳政策.
- 在 jaywalking 和非 jaywalking 场景之间比较行为模式和安全指标.
主要成果:
- 步行者表现出不稳定的动作,更高的加速和不可预测的路径.
- 与车辆相距较远的非欢乐步行者表现出更可预测的行为.
- 杰伊步行场景导致碰撞时间 (TTC) 和侵占后时间 (PET) 缩短,这表明碰撞风险更高.
结论:
- MAAIRL模型成功地学习了 jaywalking 和非 jaywalking 步行者的不同行为.
- 先进的行人模拟模型必须包含通行障碍行为模式,以便进行全面的安全分析.
- 这个框架有可能模拟复杂的真实世界交通场景,并提高安全性.
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