Autonomous rear-end collision avoidance using an electric power steering system

Abstract

A step towards fully autonomous vehicles can be achieved by autonomous collision avoidance. This paper presents the development and implementation of a rear-end collision avoidance system which consists of novel linear threat assessment, projected escape path planning with non-zero initial condition, reference generation and linear path control by an electric steering actuator using dynamic inversion. The linear threat assessment continuously calculates threat metrics associated with collision avoidance by either steering or braking. The escape path planner continuously estimates a path with constraints on the peak lateral acceleration and the required lateral deviation. The reference generator then estimates the states for avoidance according to the available friction. Linear state feedback control is applied for path following to avoid a collision in both straight-road scenarios and curved-road scenarios. The proposed approach is evaluated in simulations, and in practice on a C-segment Ford vehicle. A user-related assessment about the usefulness and satisfaction is included to conclude the paper.

Risk Assessment and Enhancement Suggestions for Automated Driving Systems through Examining Testing Collision and Disengagement Reports

“…Tese disengagements were mainly because of operator precautions, or because the ADS detected reckless behavior by other road users but did not provide sufcient reaction time for drivers to respond. In addition, some studies have designed linear path control and emergency steering assist (ESA) control to avoid rear-end collisions [24,38]. Raju et al [60] proposed a safety measure of instantaneous attention time (IHT) that quantifes the follower driver's attentiveness.…”

Time to Collision for Emergency Obstacle Avoidance

“…A fifth-order polynomial has been used to represent a lane change path (Shah et al, 2015). The length of the lane change maneuver Lxp is given by Weber (2012) as…”

Trajectory Tracking Control in Real‐Time of Dual‐Motor‐Driven Driverless Racing Car Based on Optimal Control Theory and Fuzzy Logic Method

“…e MPC method is used to realize the local trajectory planning and trajectory tracking during the collision avoidance process of the autonomous driving vehicle, which ensures the stability and safety of the autonomous vehicle during the collision avoidance process [4]. e Ford Motor Technology Research Center in the United States designed a linear state feedback controller and used the electric power steering system to realize the avoidance path tracking control of autonomous vehicles [5].…”