My project tackles the question of whether Ray can be used to quickly train autonomous vehicles using a simulator (Carla), and whether a platform robust enough for further research purposes can be built around it. Ray is an open-source framework that enables distributed machine learning applications. Distributed computing is a technique which parallelizes computational tasks, such as training a model, among many machines. Ray abstracts away the complex coordination of these machines, making it rapidly scalable. Carla is a vehicle simulator that generates data used to train a model. The bulk of the project was writing the training logic that Ray would use to train my distributed model. Imitation learning is the best fit for autonomous vehicles. Imitation learning is an alternative to reinforcement learning and it works by trying to learn the optimal policy by imitating an expert (usually a human) given a set of demonstrations. A key deliverable for the project was showcasing my trained agent in a few benchmark tests, such as navigating a complex turn through traffic. Beyond that, the broader ambition was to develop a research platform where others could quickly train and run experiments on huge amounts of Carla vehicle data. Thus, my end product is not a single model, but a large-scale, open-source research platform (RayCarla) for autonomous vehicle researchers to utilize.
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study
College of Automotive Engineering, Chongqing University（重庆大学汽车工程学院）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 mobility学院）；University of Waterloo（滑铁卢大学）
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1
本报告介绍和概述了在“识别和缩小自动驾驶汽车保证差距”（Towards Identifying and Closing Gap in Assurance of Autonomous Road Vehicles，Tigars）项目中编制的技术专题说明（TTN）。这些说明旨在支持自动驾驶汽车的开发和评估。第1部分涉及：保证-概述和问题，弹性和安全要求，开放系统视角和ML系统的形式化验证和静态分析。第2部分：模拟和动态测试，深度和多样性防御，安全信息安全分析，标准和指南。
