论文标题
基于视觉的自动化车辆的有效感知，计划和控制算法
Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles
论文作者
论文摘要
自动驾驶汽车的计算资源有限，因此需要有效的控制系统。传感器的成本和大小限制了自动驾驶汽车的发展。为了克服这些限制，本研究提出了一个有效的基于视觉自动车辆操作的框架。该框架仅需要一个单眼相机和一些便宜的雷达。所提出的算法包括一个多任务UNET（MTUNET）网络，用于提取图像特征和约束的迭代线性二次调节器（CILQR）和视觉预测性控制（VPC）模块，用于快速运动计划和控制。 MTUNET旨在同时解决车道线细分，自我车辆的标题角度回归，道路类型分类和交通对象检测任务，大约为40 fps，对于228 x 228 x 228像素RGB输入图像。然后，CILQR控制器使用MTUNET输出和雷达数据作为输入，以在仅1 ms内出现横向和纵向车辆指导的驾驶命令。特别是，包括VPC算法以将转向命令潜伏期降低到执行器延迟以下，从而防止在紧张的转弯时性能下降。 VPC算法使用MTUNET的道路曲率数据来估算当前转向角度的适当校正，以调整转弯量。将VPC算法纳入VPC-CILQR控制器中，在弯曲道路上的性能高于单独使用CILQR。我们的实验表明，不需要高清图的拟议的自主驾驶系统可以应用于当前的自动驾驶汽车中。
Autonomous vehicles have limited computational resources and thus require efficient control systems. 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 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, preventing performance degradation during tight turns. The VPC algorithm uses road curvature data from MTUNet to estimate the appropriate correction for the current steering angle at a look-ahead point to adjust the turning amount. The inclusion of the VPC algorithm in a VPC-CILQR controller leads to higher performance on curvy roads than the use of CILQR alone. Our experiments demonstrate that the proposed autonomous driving system, which does not require high-definition maps, can be applied in current autonomous vehicles.
