Cooperative adaptive cruise control with vehicle trajectory prediction using machine learning techniques

使用机器学习技术进行车辆轨迹预测的协作式自适应巡航控制

• 批准号: 571295-2021
• 负责人: Alirezaee, ShahpourSDR
• 金额: $ 1.82万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760463
• 关键词: Cooperative; adaptive; cruise; control; vehicle

The number of vehicles on roads and highways has soared in recent years and we are witnessing more traffic congestion and vehicle accidents on streets. To address these issues, car manufacturers have been developing advanced driver assistance systems (ADAS). This research proposal aims to improve adaptive cruise control (ACC) system performance by using vehicular communication and machine learning techniques. ACC relies on onboard sensors to adjust a vehicle's speed with the speed of the preceding vehicle. Since ACC performance is restricted by its sensing range, cooperative adaptive cruise control (CACC) has emerged to supplant on-board sensors with vehicular communication to exchange information between the vehicles. The main objective of this research proposal is to leverage machine learning techniques to predict vehicles trajectories in CACC. We will use the long short term memory (LSTM) deep model to predict the position of target vehicles at future time steps. This research proposal will have several benefits. Firstly, it contributes to traffic safety because the proposed system will be able to predict a collision situation and warn the driver in advance. Furthermore, our system has the potential to increase highway capacity. This is mainly because in the proposed CACC, a constant time headway gap policy will be deployed. That is to say, the distance between the following vehicles will be proportional to their speed; the higher the speed, the larger the distance. The third impact of our CACC is reducing fuel consumption because of vehicles' constant speed and less air resistance when following each other. Finally, through this international research collaboration, the University of Windsor in Canada and Leeds University in the UK can share their infrastructure, experiences, data, and methods in the field of fully automated vehicles. It can create a unique opportunity to train HQPs in the emerging fields of vehicular communication and deep learning, and exchange students and researchers in the future. HQPs will expand their network by meeting potential future collaborators, learn new skills, and gain a new perspective.

近年来，道路和高速公路上的车辆数量已经飙升，我们目睹了更多的交通拥堵和街道上的车祸。为了解决这些问题，汽车制造商一直在开发高级驾驶员援助系统（ADAS）。该研究建议旨在通过使用车辆通信和机器学习技术来提高自适应巡航控制（ACC）系统性能。 ACC依靠车载传感器以前面车辆的速度调整车辆的速度。由于ACC性能受到传感范围的限制，因此合作自适应巡航控制（CACC）已出现在带有车辆通信的板载传感器上，以在车辆之间交换信息。该研究建议的主要目的是利用机器学习技术来预测CACC中的车辆轨迹。我们将使用长期的短期内存（LSTM）深度模型来预测未来时间步骤目标车辆的位置。该研究建议将有几个好处。首先，这有助于交通安全，因为拟议的系统将能够预测碰撞情况并提前警告驾驶员。此外，我们的系统有可能提高公路能力。这主要是因为在拟议的CACC中，将部署恒定的时间前进差距策略。也就是说，以下车辆之间的距离将与它们的速度成正比。速度越高，距离越大。我们CACC的第三个影响是减少燃油消耗，因为车辆的持续速度和彼此追随时的空气阻力较小。最后，通过这项国际研究合作，加拿大温莎大学和英国利兹大学可以在全自动车辆领域共享其基础设施，经验，数据和方法。它可以创造一个独特的机会，在车辆沟通和深度学习的新兴领域培训HQP，并将来交换学生和研究人员。 HQP将通过满足潜在的未来合作者，学习新技能并获得新的观点来扩展其网络。