A Method of Autonomous Vehicle Path Planning Through Constrained Artificial Bee Colony Optimization


Seth Trojacek

Document Type


Degree Name

Master of Science (MS)


Computer Science and Info Sys

Date of Award

Spring 2021


For vehicle routing, the ability to optimize path planning and travel time, allows an or-ganization to reduce time and money to deliver cargo. This is particularly important for vehicles such as ambulances, where the fastest path to the hospital can save lives. The progress of artificial intelligence has shown remarkable results to the application of vehicle lane change optimization, as well as showing applicability for autonomous vehicles. Currently, systems for autonomous driving are mainly focused on staying in one lane, and following a set rate of speed to reduce the amount of uncertainty of the systems against human driver behaviors. However, upon a change of information, for example, debris upon a roadway, a vehicle will have to modify its planned path to best fit this change of data, while also maintaining a buffer of safety for the surrounding vehicles. Due to the time constraints for decision-making, this paper explores a method of heuristically searching for a safer path through the Artificial Bee Colony Algorithm, while also applying Constraint Satisfaction techniques to optimize its search space relative to the vehicles current position. Keywords: Artificial Bee Colony Algorithm, Path Planning, Constraint Satisfaction, Constraint Programming.


Omar El Ariss

Subject Categories

Computer Sciences | Physical Sciences and Mathematics