V2V-based vehicle risk assessment and control for lane-keeping and collision avoidance

Abstract

This article analyzes a typical numerical method to deal with maintaining vehicle's track for self-governed (autonomous) driving and obstacle avoidance. The new approach utilizes the established cost function definition consolidating the basic aspect of the dynamic conditions of the vehicle as position, orientation, and maximum allowed speed on road. The optimization processes minimize the cost function while determining the ideal track by fluctuating steering-angle and braking-ratio amplitudes. Vehicle-to-vehicle (V2V) communication framework is viably used through providing data on maximal road speed and road's obstacle dimensions. The parametric definition of obstacles creates an adaptable domain for low and high-speed simulations. The minimal number of influential optimization variables ensures a steady and direct generation of ideal results. By the current novel approach, we are independently able to move the vehicle on an arbitrary track approximated by low-order polynomials. Simulation tests are performed under vehicle's speeds of 10m/s, 18 m/s whilst utilizing most important features of Vehicle-to-vehicle communication systems.