使用时间序列生成对抗网络生成车辆变换车道的场景,并采用自适应参数优化策略
Ye Li1, Fanming Zeng1, Chunyang Han2
1School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China.
Accident; analysis and prevention
|June 8, 2024
概括
本研究引入了一种使用TimeGAN生成多种连接和自动化车辆 (CAV) 测试场景的新方法. 该方法有效地识别罕见的高风险情况,以提高自动驾驶的安全性.
科学领域:
- 人工智能的人工智能
- 自主系统 自主系统
- 运输工程 运输工程
背景情况:
- 连接和自动驾驶汽车 (CAV) 承诺提高安全性和效率.
- 对各种场景进行严格的测试对于CAV部署至关重要.
- 现有的方法在场景多样性和全面评估方面面临挑战.
研究的目的:
- 为CAV测试提出一种创新方法,用于生成各种车辆改变车道的场景.
- 为了应对测试场景多样性和评估全面性的挑战.
- 提高自动驾驶技术的安全性和可靠性.
主要方法:
- 利用了一个时间序列生成对抗网络 (TimeGAN) 与一个自适应参数优化策略 (APOS).
- 训练的TimeGAN具有有限的参数组合子集 (13.3%).
- 评估多样性,忠实性和实用性的生成场景;采用车道变动风险指数 (LCRI) 来识别对抗性情况.
主要成果:
- 成功训练了一个TimeGAN来生成大量的车道更换场景.
- 生成的场景有效地捕捉了各种各样的驾驶情况.
- 该方法产生了27倍多的对抗性案例,与真实场景相比,平均风险高1.8倍.
结论:
- 拟议的基于TimeGAN的方法增强了CAV测试场景的生成.
- 该方法在发现关键安全漏洞方面是有效的.
- 这有助于更全面,更可靠的自动驾驶测试和技术.
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