An Exploratory Goal-Based Modeling of Optimized Collision Avoidance Action Selection in Autonomous Vehicles

Abstract

The collision-free path planning is crucial for an autonomous vehicle. It saves life and helps to complete the task in time. The computational intelligence mimics human intelligence and solves these types of problems in which conventional techniques fail to provide optimal solutions. In literature, computational intelligence techniques except evolutionary techniques have not been utilized for land vehicles. In this regard, we employ clonal selection algorithms for collision avoidance in an autonomous vehicle.   To check the effectiveness of our proposed scheme, we compare the performance of the clonal selection algorithm with the genetic algorithm. The results show that the clonal selection algorithm performs better than the genetic algorithm in terms of computation time and also avoids accident within the required time to avoidance.