TUMCREATE, 1 CREATE Way, #10-02 CREATE Tower, Singapore 138602, Singapore.
Institute of Automotive Technology, Technical University of Munich, Boltzmannstr. 15, 85748 Munich, Germany.
Sensors (Basel). 2021 Feb 3;21(4):1039. doi: 10.3390/s21041039.
Connected and autonomous vehicles (CAVs) could reduce emissions, increase road safety, and enhance ride comfort. Multiple CAVs can form a CAV platoon with a close inter-vehicle distance, which can further improve energy efficiency, save space, and reduce travel time. To date, there have been few detailed studies of self-driving algorithms for CAV platoons in urban areas. In this paper, we therefore propose a self-driving architecture combining the sensing, planning, and control for CAV platoons in an end-to-end fashion. Our multi-task model can switch between two tasks to drive either the leading or following vehicle in the platoon. The architecture is based on an end-to-end deep learning approach and predicts the control commands, i.e., steering and throttle/brake, with a single neural network. The inputs for this network are images from a front-facing camera, enhanced by information transmitted via vehicle-to-vehicle (V2V) communication. The model is trained with data captured in a simulated urban environment with dynamic traffic. We compare our approach with different concepts used in the state-of-the-art end-to-end self-driving research, such as the implementation of recurrent neural networks or transfer learning. Experiments in the simulation were conducted to test the model in different urban environments. A CAV platoon consisting of two vehicles, each controlled by an instance of the network, completed on average 67% of the predefined point-to-point routes in the training environment and 40% in a never-seen-before environment. Using V2V communication, our approach eliminates casual confusion for the following vehicle, which is a known limitation of end-to-end self-driving.
车联网（CAV）可以减少排放，提高道路安全性，提升乘坐舒适度。多辆 CAV 可以组成一个车距紧密的 CAV 车阵，进一步提高能源效率、节省空间并减少旅行时间。迄今为止，针对城市环境中 CAV 车阵的自动驾驶算法研究还很少。因此，本文提出了一种端到端的 CAV 车阵自驾驶架构，将感知识别、规划和控制集成在一起。我们的多任务模型可以在车阵中的领航车和跟随车之间切换驾驶任务。该架构基于端到端的深度学习方法，使用单个神经网络预测转向和油门/刹车等控制命令。该网络的输入是来自前置摄像头的图像，并通过车对车（V2V）通信传输的信息进行增强。该模型使用在具有动态交通的模拟城市环境中捕获的数据进行训练。我们将我们的方法与端到端自动驾驶研究中的不同概念（如递归神经网络的实现或迁移学习）进行了比较。在模拟环境中进行了实验，以在不同的城市环境中测试模型。由两辆车辆组成的 CAV 车阵，每辆车由网络的一个实例控制，在训练环境中平均完成了 67%的预设点对点路线，在从未见过的环境中完成了 40%。通过 V2V 通信，我们的方法消除了跟随车的偶然困惑，这是端到端自动驾驶的已知限制。
