用多个小模型动态选择驾驶行为，提升自动驾驶安全与可解释性。
Towards Selection and Transition Between Behavior-Based Neural Networks for Automated Driving
- 通过多个小型神经网络分别处理不同驾驶任务
- 实现在不同行为间平滑过渡，提升系统稳定性
- 适合关注自动驾驶可解释性与安全性的研究者
自动驾驶技术快速发展，主要依赖复杂的端到端深度神经网络系统。尽管这些系统有效，但其复杂性导致行为难以理解，带来安全隐患。本文提出一种行为选择器（Behavior Selector），采用多个小型人工神经网络（ANN）分别管理车道保持、转向等不同驾驶任务。相比单一大型网络，该方法减轻计算负担，减少对训练数据的依赖，并提升可解释性。系统可在实时运行中根据当前车速和朝向动态选择最合适的神经网络，确保行为切换平稳。实验在AirSim仿真环境中进行，验证了该方案的有效性。
原文摘要 · Abstract (English)
Autonomous driving technology is progressing rapidly, largely due to complex End To End systems based on deep neural networks. While these systems are effective, their complexity can make it difficult to understand their behavior, raising safety concerns. This paper presents a new solution a Behavior Selector that uses multiple smaller artificial neural networks (ANNs) to manage different driving tasks, such as lane following and turning. Rather than relying on a single large network, which can be burdensome, require extensive training data, and is hard to understand, the developed approach allows the system to dynamically select the appropriate neural network for each specific behavior (e.g., turns) in real time. We focus on ensuring smooth transitions between behaviors while considering the vehicles current speed and orientation to improve stability and safety. The proposed system has been tested using the AirSim simulation environment, demonstrating its effectiveness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。