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首页> 外文期刊>IEEE sensors journal >A Lightweight Simulation Framework for Learning Control Policies for Autonomous Vehicles in Real-World Traffic Condition
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A Lightweight Simulation Framework for Learning Control Policies for Autonomous Vehicles in Real-World Traffic Condition

机译：一种轻量级仿真框架，用于真实世界交通条件下自治车辆的控制政策

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摘要

We present a new simulation framework for learning control policies for autonomous vehicles (AVs) based on real-world vehicle data and maps. The framework we propose consists of three major components: a) creating a detailed lane-level map (i.e., a high definition (HD) map) for a region of interest, b) generating an environment on the HD-map using the sensor outputs (e.g., GPS, radar) from vehicles driving in the same region, and c) learning a control policy based on the realistic environment constructed. We created the lane-level HD-maps using open street maps (OSM) and aerial imagery, fromwhichwe extracted the lane-level marking and edge features. The extracted image features are then utilized to calculate higher level attributes (e.g., curvature, heading, cross-sections etc.) for each point in the HD-map. The data acquired from vehicle sensors is combined with the constructed map to create a realistic environment. Based on the constructed environment, we learned a policy to control the vehicle laterally using a reinforcement learning algorithm and longitudinally using proportional-integral-derivative (PID) controller. Our experimental results show that the proposed framework works well, offering a flexible and scalable solution for learning control policies for AVs in realistic environments.
机译：我们为基于现实世界的车辆数据和地图提供了一种用于学习自动车辆（AVS）的学习政策的新模拟框架。我们提出的框架由三个主要组件组成：a）创建一个感兴趣区域的详细车道级地图（即，使用传感器输出在高清地图上生成环境（例如，GPS，雷达）从在同一区域行驶的车辆，以及C）基于构造的现实环境学习控制策略。我们使用Open Street Maps（OSM）和空中图像创建了车道级高清地图，从其提取了通道级标记和边缘功能。然后，利用提取的图像特征来计算HD-MAP中的每个点的更高级别属性（例如，曲率，标题，横截面等）。从车辆传感器获取的数据与构造的地图相结合以创建现实环境。基于构造的环境，我们学习了使用比例积分衍生（PID）控制器纵向控制车辆的策略来控制车辆。我们的实验结果表明，该建议的框架运行良好，为逼真环境中的AVS学习控制策略提供灵活和可扩展的解决方案。

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