自动驾驶控制器具有神经形状的尖端神经网络
Raz Halaly1, Elishai Ezra Tsur1
1Neuro-Biomorphic Engineering Lab, Department of Mathematics and Computer Science, Open University of Israel, Ra'anana, Israel.
Frontiers in neurorobotics
|August 28, 2023
概括
神经形态控制器表现出与传统自动驾驶方法相比具有竞争力的性能. 使用100-1000个神经元和混合方法的优化设计是关键,尽管高速带来了挑战.
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
- 计算神经科学是一种神经科学.
背景情况:
- 自动驾驶系统依赖于复杂的控制算法.
- 神经形态计算,灵感来自大脑,提供节能计算框架.
- 尖端神经网络 (SNN) 是神经形态控制的一个关键组成部分.
研究的目的:
- 探索四个著名的自动驾驶控制器的神经形态实现.
- 评估和比较神经形态控制器的性能与传统的基于CPU的实现.
- 为自主控制中神经形态架构设计提供指导方针.
主要方法:
- 用于实施和测试控制器的物理意识模拟框架.
- 探索了纯追求,斯坦利,PID和模型预测控制 (MPC) 的神经形态实现.
- 用不同的内在参数进行广泛的评估,并与基于CPU的模型进行比较.
主要成果:
- 神经形态模型表现出与传统对应物相比具有竞争力的性能.
- 在大多数模型中,用100-1,000个神经元达到最佳性能.
- 混合常规和神经形态设计 (例如,MPC) 显示出有希望的结果.
- 在15m/s以上的速度下,神经形态模型中观察到的性能退化.
结论:
- 神经形态控制是自动驾驶的可行和节能方法.
- 仔细调整参数和神经元资源对于神经形态控制器优化至关重要.
- 混合架构为克服局限性和增强神经形态控制系统提供了一条道路.
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