Optimization of an Autonomous Car Controller Using a Self-Adaptive Evolutionary Strategy

Optimization of an Autonomous Car Controller Using a Self-Adaptive Evolutionary Strategy

Blog Article

Autonomous cars control the steering wheel, acceleration and the brake pedal, the gears and the clutch using sensory information from multiple sources.Like a human driver, it understands the current situation on the roads from the live streaming of sensory values.The decision-making module often suffers from the limited range of sensors and complexity due to the large number Inline - Wheels Court of sensors and actuators.Because it is tedious and difficult to design the controller manually from trial-and-error, it is desirable to use intelligent optimization algorithms.

In this work, we propose optimizing the parameters of an autonomous car controller using self-adaptive evolutionary strategies (SAESs) which co-evolve solutions and mutation steps for each parameter.We also describe how the most generalized parameter set can be retrieved from the process of optimization.Open-source car racing simulation software (TORCS) is used to Beaded Headstall test the goodness of the proposed methods on 6 different tracks.Experimental results show that the SAES is competitive with the manual design of authors and a simple ES.