Driving Simulator for Connected Autonomous Vehicles

联网自动驾驶汽车驾驶模拟器

• 批准号: 2593447
• 金额: --
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2593447
• 关键词: Driving; Simulator; Connected; Autonomous; Vehicles

According to the World Health Organisation, the number of traffic accident-related deaths remains unacceptably high, with over 1.3 million people dying each year globally. This number continues to rise due to growing population and increasing motor vehicle uptake. It has been reported that vast majority of trauma-related fatalities occur in the prehospital phase, while nearly a half of those are preventable. Therefore, timely and precise crash detection systems and appropriate response from emergency services are critical for reducing preventable deaths. Existing systems are imprecise, unreliable, and computationally heavy. An informal ongoing collaboration with Volkswagen, established during the summer project, allowed to explore multiple computer vision methods and deep learning algorithms for accident detection applied to an existing traffic surveillance infrastructure. The project team has been authorised to access every traffic surveillance camera in the city of Carmel, Indiana. This allows testing deep learning algorithms on real-time data around-the-clock, during different times of the day and weather conditions, and on varying road layouts. As an outcome of the summer project, fundamental principles of computer vision and deep learning were explored and multitude of software tools, necessary for the upcoming PhD research, were utilised. The groundwork for my own algorithmic development for this ongoing collaboration was established through deployment and benchmarking of state-of-the-art algorithms.With human error being the predominant cause of vast majority of road accidents, computer vision-based accident detection systems can reduce preventable deaths. Once the appropriate level of technological sophistication is reached, connected autonomous vehicles will eliminate the human factor. The major PhD research is focused on contributing towards developing an autonomous driving simulator, which is an effective tool for generating and validating advanced driver-assistance systems. Ongoing developments with Volkswagen provide technical training and research data that will be used for the simulator. For instance, inference data from Carmel, saved in CSV format, will be combined with OpenStreetMap data and used for developing virtual and mixed reality scenarios and modelling road networks in OpenDRIVE format. Furthermore, computer vision algorithms will be modified and optimised for the vehicle camera. The simulator will then be connected to the powertrain dyno to allow hardware-in-the-loop control of a physical vehicle and its subsystems.For Volkswagen, this research aims to develop a traffic accident detection feature that would reduce trauma-related fatalities that occur in the prehospital phase following a traffic accident. For IAAPS, it will contribute towards putting together a hardware-agnostic Advanced Driver Assistance System simulation software system with the ability to easily upgrade the hardware setup to support motion platforms and 360-degree projection screens. With simulation testing being an efficient way of validating autonomous technology, such expertise will be beneficial for the institute, empowering the organisation to become a trailblazer in the automotive industry, making its inevitable future closer.

根据世界卫生组织的数据，与交通事故有关的死亡人数仍然高得令人无法接受，全球每年有130多万人死亡。由于人口增长和机动车使用量增加，这一数字继续上升。据报告，绝大多数与创伤有关的死亡发生在入院前阶段，而其中近一半是可以预防的。因此，及时和精确的碰撞检测系统以及应急服务部门的适当反应对于减少可预防的死亡至关重要。现有的系统是不精确的，不可靠的，计算量大。在夏季项目期间，与大众汽车建立了非正式的持续合作，可以探索多种计算机视觉方法和深度学习算法，用于现有交通监控基础设施的事故检测。该项目小组已被授权访问印第安纳州卡梅尔市的每一个交通监控摄像头。这允许在一天中的不同时间和天气条件以及不同的道路布局中全天候测试实时数据的深度学习算法。作为暑期项目的成果，计算机视觉和深度学习的基本原理得到了探索，并利用了即将到来的博士研究所需的多种软件工具。通过部署和对最先进的算法进行基准测试，我为这项持续合作的算法开发奠定了基础。由于人为错误是绝大多数道路事故的主要原因，基于计算机视觉的事故检测系统可以减少可预防的死亡。一旦达到适当的技术复杂程度，联网自动驾驶汽车将消除人为因素。博士研究的重点是开发自动驾驶模拟器，这是生成和验证先进驾驶辅助系统的有效工具。与大众汽车正在进行的开发提供了将用于模拟器的技术培训和研究数据。例如，来自卡梅尔的以CSV格式保存的推理数据将与OpenStreetMap数据相结合，用于开发虚拟和混合现实场景，并以OpenDRIVE格式建模道路网络。此外，还将针对车载摄像头修改和优化计算机视觉算法。然后，模拟器将连接到动力总成dyno，以实现物理车辆及其子系统的硬件在环控制。对于大众汽车，这项研究旨在开发一种交通事故检测功能，以减少交通事故后院前阶段发生的创伤相关死亡事故。对于IAAPS，它将有助于整合一个与硬件无关的高级驾驶员辅助系统模拟软件系统，该系统能够轻松升级硬件设置，以支持运动平台和360度投影屏幕。由于模拟测试是验证自动驾驶技术的有效方法，这些专业知识将有利于该研究所，使该组织成为汽车行业的开拓者，使其不可避免的未来更加接近。