  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.
