人类启发的情绪和注意力编码用于自动驾驶汽车的决策:一个车道更换时间优化案例
Tianyuan Han1, Tingyu Liu2, Qiong Bao1
1School of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, China.
Accident; analysis and prevention
|March 14, 2026
概括
自动驾驶汽车 (AV) 现在可以更安全,更有效地改变车道,使用一种由人类情感和注意力启发的新框架. 这种方法模仿了人类的驾驶行为,提高了交通流量,减少了混合交通中的事故.
科学领域:
- 自主系统 自主系统
- 认知科学 认知科学
- 神经科学是一个神经科学.
背景情况:
- 自动驾驶汽车 (AV) 在混合交通中改变车道是由于协调要求而面临的挑战.
- 保守的AV策略可能会降低效率并造成中断.
- 需要类似人类的决策,以实现灵活可靠的自动驾驶车道更换.
研究的目的:
- 开发一种情绪和注意力编码框架,用于类似人类的AV车道变化.
- 为了使自动驾驶汽车能够在复杂的交通场景中适应性优化车道更改时间.
主要方法:
- 利用了认知能量理论,减弱器理论和前景理论.
- 为驾驶员的兴奋,体验,注意力和情绪效用而构建的神经编码过程.
- 引入了情绪效用模型 (EUM) 和类似人类的车道更换决策 (HLD) 方法.
主要成果:
- 在3西格玛规则下,HLD方法实现了超过99.8%的车道更换率.
- 换车道的时间与实际驾驶员的行为紧密相匹配,同时提高了安全性.
- 欧盟驻马团适应性地调整了风险权重,以改善效用和风险平衡.
结论:
- 拟议的框架使自动驾驶汽车能够像人类一样改变车道,提高效率和安全.
- 在动态环境中,EUM是平衡风险和优化决策的关键.
- 这种方法为各种复杂场景中的AV决策提供了洞察力.
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