使用改进的近距离政策优化优化自动驾驶汽车性能优化
1Computer Technologies, Necmettin Erbakan University, 42360 Seydişehir, Turkey.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
自动驾驶汽车使用增强的近距离政策优化 (PPO) 和莱维飞行来改进决策. 这种Lévy飞行增强的PPO (LFPPO) 算法可以显著减少碰撞,并在复杂的交通场景中提高成功率.
科学领域:
- 自动驾驶自动驾驶的自动驾驶
- 强化学习是一种强化学习.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自动驾驶汽车需要在动态的城市环境中进行强有力的决策.
- 邻近政策优化 (PPO) 提供了稳定性,但受到了有限的探索.
- 现有的方法在适应性和探索各种策略方面扎.
研究的目的:
- 增强自动驾驶PPO算法的探索能力.
- 提高适应能力,减少局部最小值在PPO中被困.
- 为自动驾驶汽车开发一个更稳定,更有效的学习机制.
主要方法:
- 将Lévy飞行的混乱探索集成到PPO算法中,创建LFPPO.
- 从CARLA模拟器收集实时数据 (速度,位置,交通信号) 通过Apache Kafka进行处理.
- 在模拟的城市交通中对标准PPO和LFPPO性能进行比较分析.
主要成果:
- LFPPO取得了99%的成功率,明显超过了PPO的81%的成绩.
- 与PPO的19%相比,LFPPO将碰撞率降低到1%.
- 改进的算法显示出卓越的稳定性和更高的奖励.
结论:
- 通过使用莱维飞行和实时数据,LFPPO有效地克服了PPO的勘探局限性.
- LFPPO算法为自动驾驶系统提供了增强的安全性和探索性.
- 这种方法比目前最先进的方法有了显著的进步.
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