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 vehicle understeer 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 leads to higher performance than CILQR alone; this controller can minimize the influence of command lag, maintaining the ego car's speed and lateral offset at 76 km/h and within 0.52 m, respectively, on a simulated road with a curvature of 0.03 1/m. 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：自动驾驶汽车的计算资源有限，因此其控制系统必须高效。传感器的成本和尺寸限制了自动驾驶汽车的发展。为克服这些限制，本研究提出了一种基于视觉的自动汽车高效运行框架；该框架仅需单目摄像头和少量低成本雷达。所提算法包括用于图像特征提取的多任务UNet（MTUNet）网络，以及用于快速运动规划和控制的约束迭代线性二次调节器（CILQR）和视觉预测控制（VPC）模块。MTUNet被设计用于同时解决车道线分割、自车航向角回归、道路类型分类和交通目标检测等任务，处理228×228像素的RGB输入图像时帧率约为40 FPS（帧/秒）。随后，CILQR控制器以MTUNet输出和雷达数据为输入，在仅1毫秒内生成用于横向和纵向车辆引导的驾驶指令。特别地，引入VPC算法以将转向指令延迟降至致动器延迟以下，从而避免车辆急转弯时的转向不足。VPC算法利用MTUNet提供的道路曲率数据，在当前转向角的前瞻点处预估修正量以调整转弯幅度。将VPC算法集成至VPC-CILQR控制器中，其性能优于单独使用CILQR；该控制器能最小化指令滞后的影响，在曲率为0.03 1/m的模拟道路上，将自车速度维持在76 km/h，横向偏移控制在0.52米以内。实验表明，所提出的自动驾驶系统无需高精度地图，可应用于现有自动驾驶车辆中。