Part A. Decision-making and path planning based on skilled driver’s performance for emergency obstacle avoidance

Abstract

Emergency Lane Change (ELC) is a strategic maneuver for collision avoidance (CA) at high speeds where there is an accident risk. In this paper, two algorithms are designed for CA at high speeds based on the vehicle’s initial speed and the distance of the obstacle. The first algorithm selects the more suitable case between Automatic Emergency Braking (AEB) and ELC systems to prevent an accident. Suppose there was no choice except a rapid Lane Change (LC); the second algorithm does path planning for an ELC. The most critical issue in the ELC is time because this maneuver’s duration is less than 2 or 3 s on the dry or wet road. So, designing a fast and safe path planning algorithm is very important. To solve this problem, with a seven-degrees of freedom vehicle dynamic’s equations and the skilled driver behavior manner in the ELC maneuver, scrollable and stable paths for initial speeds of 75–125 km/h are extracted. Then, a neural network (NN) is trained based on this simulated data. The main feature of this network is to design stable paths as fast as possible. Also, this network can plan the paths in the various road friction coefficients between 0.5 and 1. The trajectory is selected based on two geometric constraints including, including (1) not colliding with obstacles and (2) not crossing the road boundary.