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.

This paper considers the vehicle control process in emergency lane change (ELC). The control method is divided into two parts: before crossing the obstacle and after crossing it. The first part is done by a feed-forward controller inspired by the driver’s behavior pattern in the ELC maneuver. This controller is compensated by a neural network system and two proportional-integral (PI) and proportional-derivative (PD) controllers, which correct the output of the feed-forward controller to follow the path more accurately. The second part consists of two PD controllers responsible for controlling and steering the vehicle in the main path after crossing the obstacle. The main novelty of the proposed method is that the computational burden is low because of using a feed-forward controller, which is the main source of the calculation’s burden. So as a result, the controller system can cover the ELC maneuver in under 2 seconds in various lateral displacements to bypass the obstacle and prevent collisions. The co-simulations are performed in CarSim software and MATLAB/Simulink, which shows that the control system can appropriately steer the vehicle and follow the desired at different speeds, in the presence of road friction and parameter uncertainty.