Autonomous vehicles have limited computational resources; hence, their control systems must be efficient. The cost and size of sensors have limited the development of self-driving cars. To overcome these restrictions, this study proposes an efficient framework for the operation of vision-based automatic vehicles; the framework requires only a monocular camera and a few inexpensive radars. The proposed algorithm comprises a multi-task UNet (MTUNet) network for extracting image features and constrained iterative linear quadratic regulator (CILQR) and vision predictive control (VPC) modules for rapid motion planning and control. MTUNet is designed to simultaneously solve lane line segmentation, the ego vehicle's heading angle regression, road type classification, and traffic object detection tasks at approximately 40 FPS (frames per second) for 228 x 228 pixel RGB input images. The CILQR controllers then use the MTUNet outputs and radar data as inputs to produce driving commands for lateral and longitudinal vehicle guidance within only 1 ms. In particular, the VPC algorithm is included to reduce steering command latency to below actuator latency to prevent self-driving vehicle performance degradation during tight turns. The VPC algorithm uses road curvature data from MTUNet to estimate the correction of the current steering angle at a look-ahead point to adjust the turning amount. Including the VPC algorithm in a VPC-CILQR controller on curvy roads leads to higher performance than CILQR alone. Our experiments demonstrate that the proposed autonomous driving system, which does not require high-definition maps, could be applied in current autonomous vehicles.

翻译：自动驾驶车辆的计算资源有限，因此其控制系统必须高效。传感器成本与尺寸限制了自动驾驶汽车的发展。为解决这些限制，本研究提出了一种基于视觉的自动驾驶车辆高效运行框架；该框架仅需单目摄像头和少量低成本雷达。所提出算法包含多任务UNet（MTUNet）网络用于提取图像特征，以及约束迭代线性二次型调节器（CILQR）与视觉预测控制（VPC）模块用于快速运动规划与控制。MTUNet被设计为能够同时完成车道线分割、自车航向角回归、道路类型分类及交通目标检测任务，对228×228像素的RGB输入图像处理速度约为40 FPS（帧/秒）。CILQR控制器随后利用MTUNet输出与雷达数据作为输入，在1毫秒内生成用于车辆横向与纵向引导的驾驶指令。特别地，VPC算法被引入以减少转向指令延迟至低于执行器延迟，从而防止自动驾驶车辆在急转弯过程中性能下降。VPC算法利用来自MTUNet的道路曲率数据，估计当前转向角在预瞄点处的修正量，以调整转弯幅度。在弯道路况下，将VPC算法纳入VPC-CILQR控制器相比单独使用CILQR能够实现更高性能。我们的实验表明，所提出无需高清地图的自动驾驶系统可应用于当前自动驾驶车辆中。