Adaptive Decentralized Emergent Behavior Based Autonomous Platooning

Vehicle platooning has been shown to significantly improve road safety and driver experience, as well as increase road capacity, reduce fuel consumption, and thus green house emission. We present the Adaptive Decentralized Emergent-based Platooning system, or ADEPT for short, an emergent behavior based autonomous platooning approach that adopts a bottom-up, biologically inspired approach where each vehicle follows a set of simple rules to execute platoon maneuvers. In this paper, we evaluate ADEPT’s network performance against centralized platooning using a platooning simulator and show that ADEPT is able to yield superior network utilization. Additionally, we study the interplay between our emergent platooning system and vehicle characteristics and dynamics such as engine performance, weight, center of mass, friction, speed, and acceleration, and evaluate using a robot simulator equipped with an open dynamics physics engine. Experimental results demonstrate that ADEPT’s controller is string stable when subject to different maneuvers and disturbances. In addition to evaluating ADEPT’s controller stability, we propose methods that enable ADEPT to handle real-world conditions such as curved roads, vehicle following and obstacle avoidance. Our experimental results show that our dynamic radius of curvature determination method helps maintain inter-vehicle gap at 99% of the desired gap including on curved roads. Even during disturbance events, the gap errors are kept within the desired deviation. Using inspiration from lane assist technology, we propose a vehicle following technique that, according to our experimental results, yields high accuracy.