论文标题
研究基于深度学习的非结构环境中稳定障碍避免控制策略的稳定障碍控制策略
Research on Stable Obstacle Avoidance Control Strategy for Tracked Intelligent Transportation Vehicles in Non-structural Environment Based on Deep Learning
论文作者
论文摘要
现有的智能驾驶技术通常在平衡平稳驾驶和快速避免障碍物方面存在问题，尤其是在车辆处于非结构环境中，并且在紧急情况下容易出现不稳定。因此，这项研究提出了一种自主障碍控制策略，该策略可以根据注意力驾驶的想法有效地基于注意力的短期记忆（注意LSTM）深度学习模型来确保车辆稳定性。首先，我们设计了自主障碍避免控制规则，以确保无人车辆的安全。其次，我们改善了自动障碍避免控制策略，并结合了特殊车辆的稳定性分析。第三，我们通过实验构建了深度学习障碍物控制，该系统的平均相对误差为15％。最后，该控制策略的稳定性和准确性得到了数值和实验验证。这项研究中提出的方法可以确保无人车辆可以在平稳行驶时成功避免障碍。
Existing intelligent driving technology often has a problem in balancing smooth driving and fast obstacle avoidance, especially when the vehicle is in a non-structural environment, and is prone to instability in emergency situations. Therefore, this study proposed an autonomous obstacle avoidance control strategy that can effectively guarantee vehicle stability based on Attention-long short-term memory (Attention-LSTM) deep learning model with the idea of humanoid driving. First, we designed the autonomous obstacle avoidance control rules to guarantee the safety of unmanned vehicles. Second, we improved the autonomous obstacle avoidance control strategy combined with the stability analysis of special vehicles. Third, we constructed a deep learning obstacle avoidance control through experiments, and the average relative error of this system was 15%. Finally, the stability and accuracy of this control strategy were verified numerically and experimentally. The method proposed in this study can ensure that the unmanned vehicle can successfully avoid the obstacles while driving smoothly.
