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

近年来，道路和高速公路上的车辆数量激增，我们目睹了更多的交通拥堵和街道上的车辆事故。为了解决这些问题，汽车制造商一直在开发先进的驾驶员辅助系统（ADAS）。本研究旨在利用车辆通讯与机器学习技术，改善自适应巡航控制系统的效能。ACC依靠车载传感器根据前车的速度调整车速。由于ACC的性能受到其传感范围的限制，协同自适应巡航控制（CACC）已经出现，以取代车载传感器与车辆通信，以交换车辆之间的信息。该研究提案的主要目标是利用机器学习技术来预测CACC中的车辆轨迹。我们将使用长短期记忆（LSTM）深度模型来预测目标车辆在未来时间步长的位置。这项研究计划将有几个好处。首先，它有助于交通安全，因为所提出的系统将能够预测碰撞情况并提前警告驾驶员。此外，我们的系统有可能增加公路的容量。这主要是因为在建议的CACC中，将部署恒定的时间间隔政策。也就是说，后面的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。我们的CACC的第三个影响是减少燃料消耗，因为车辆的恒定速度和较小的空气阻力时，跟随对方。最后，通过此次国际研究合作，加拿大温莎大学和英国利兹大学可以分享各自在全自动汽车领域的基础设施、经验、数据和方法。它可以创造一个独特的机会，在车辆通信和深度学习的新兴领域培训HQP，并在未来交换学生和研究人员。HQP将通过会见潜在的未来合作者来扩大他们的网络，学习新技能，并获得新的视角。

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.