精品文档---下载后可任意编辑主动避撞系统安全距离模型及其控制方法讨论中期报告摘要主动避撞系统是现代汽车智能化的重要组成部分，其设计和实现涉及到多个学科领域。为了实现自动驾驶和安全驾驶等目标，主动避撞系统需要准确地预测车辆的运动状态，并同时保持合适的安全距离。本讨论基于机器学习和控制理论，着重探究主动避撞系统的安全距离模型和控制方法。本文首先介绍了主动避撞系统的背景和现状，并分析了安全距离模型对车辆运动状态的预测和控制的重要性。其次，本文提出了一种结合深度学习和传统控制方法的安全距离模型，使用历史观测数据训练神经网络，预测车辆未来运动状态，然后通过传统控制算法保持车辆与前车之间的安全距离。最后，本文对所提出的方法进行了实验验证，结果表明所提出的安全距离模型和控制方法可以有效地保持车辆与前车之间的安全距离，在各种路面和交通条件下均具有较好的适应性和鲁棒性。关键词：主动避撞系统，安全距离模型，深度学习，传统控制方法，实验验证AbstractActive collision avoidance system is an important part of the intelligentization of modern automobiles, and its design and implementation involve multiple disciplinary fields. In order to achieve the goals of autonomous driving and safe driving, active collision avoidance system needs to accurately predict the motion state of vehicles and maintain an appropriate safety distance at the same time. Based on machine learning and control theory, this study focuses on exploring the safety distance model and control method of active collision avoidance system. This paper first introduces the background and current situation of active collision avoidance system, and analyzes the importance of safety distance model for predicting and controlling the motion state of vehicles. Secondly, this paper proposes a safety distance model combining deep learning and traditional control methods, which uses historical observation data to train neural network, predicts the future motion state of vehicles, and then maintains the safety distance between vehicles and the front vehicle through traditional control algorithm. Finally, this paper 精品文档---下载后可任意编辑verifies the proposed method through experiments, and the results show that the proposed safety distance model and control method can effectively maintain the safety distance between vehicles and the front vehicle, and have good adaptability and robustness under various road and traffic conditions.Keywords: active collision avoidance system, safety distance model, deep learning, traditional control method, experimental verification.
