整合网络物理系统与嵌入式技术来控制自动驾驶汽车的驾驶
Manal Abdullah Alohali1, Hamed Alqahtani2, Abdulbasit Darem3
1Department of Information Systems, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
PeerJ. Computer science
|June 26, 2025
概括
这项研究通过使用双深Q网络 (DDQN) 和FPGA硬件加速来增强自动驾驶网络物理系统 (CPS). 结合方法在不确定的驾驶条件下提高了决策可靠性和效率.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 计算机工程 计算机工程
背景情况:
- 自动驾驶汽车的网络物理系统 (CPS) 在动态环境中面临着挑战.
- 深度强化学习 (DRL) 在不确定的环境中与安全性和最佳行为作斗争.
- 由于DLR难以理解动态奖励系统,因此限制了其在自动驾驶领域的应用.
研究的目的:
- 在不确定的条件下提高自动驾驶CPS的适应性和决策能力.
- 解决当前DRL模型在确保安全和最佳行为方面的局限性.
- 研究算法增强与硬件加速的整合,以实现实时增强学习.
主要方法:
- 整合双重深度Q网络 (DDQN) 以提高代理的适应性.
- 开发一个结构化的奖励系统,以实时适应波动.
- 现场可编程门阵列 (FPGA) 的实现,用于实时强化学习执行和硬件加速.
主要成果:
- 结合FPGA硬件加速和DDQN,显著提高了计算效率.
- 在不确定的自动驾驶场景中提高决策可靠性.
- 包括碰撞率,行为相似性,行程距离和速度控制在内的关键性能指标显示出积极的结果.
结论:
- 基于FPGA的硬件加速与DDQN有效地解决了自动驾驶CPS的不确定性.
- 该研究推进了CPS中的强化学习应用,提高了安全性和效率.
- 未来的研究可以探索现实世界的概括,自适应奖励和自主系统的可扩展硬件.
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