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.

Part B: Steering and control of an autonomous vehicle in emergency lane change maneuvers based on skilled driver’s performance

“…The angle change of the vehicle steering wheel is continuous in the natural condition and is changing at a specific rate which this rate is considered 30 deg/s in this case (Neisy Minaei and Ghaffari, 2022). When the angle calculated by the control system is more than the vehicle's actual rate, the modifier block limits this value and modifies that by equation (3).…”

“…In the environment block, Xarv is the distance from the obstacle, Vx0 is the initial longitudinal speed of the vehicle, μ is the road friction coefficient, and YObs is the obstacle width. In the path planning block (Neisy Minaei and Ghaffari, 2022), the maximum vehicle lateral displacement (Yf) is calculated. The driver control model comprises a sinusoidal pattern and a neural network algorithm.…”