UC Santa Cruz

Vehicles following each other in close-proximity in the form of a platoon reduce fuel consumption due to reduction in air drag saving up to 5% for the first vehicle and 10% for the following vehicles. Introduction of vehicle-to-vehicle communication has led to further decrease in inter-vehicular spacing, enabling further reduction in fuel consumption and improving the road capacity by up to 166%. Dynamic platoon formation and dissolution is generally governed by the lead vehicle, leading to a centralized approach. This suffers from various drawbacks such as single point of failure and performance bottlenecks resulting in serialization of maneuvers and shorter platoon length. A decentralized approach mitigates these issues and is traditionally realized using the "top-down" approach by first identifying the maneuvers and then executing the required steps and exchanging the necessary messages. Such decentralized platooning systems, which are known as “Deliberate Platooning”, suffer from higher complexity and reduced flexibility. This dissertation presents ADEPT – Adaptive Decentralized Emergent-behavior based PlaTooning, that aims at mitigating the shortcomings of "traditional" decentralized platooning systems. ADEPT uses a novel decentralized automated vehicle platooning approach inspired by nature's "emergent behavior” commonly found in biological systems. In ADEPT, each vehicle follows a set of simple rules when they need to interact in order to carry out the maneuvers. Platooning maneuvers such as join, exit, and merge “emerge” as a result of vehicles following these “emergent rules”. Through extensive simulation experiments using a platooning-enabled vehicular network simulator driven by a wide range of scenarios, we demonstrate that ADEPT yields superior performance in terms of maneuver time and communication overhead, especially in multi-vehicle multi-maneuver scenarios. Additionally, we devise mechanisms for obstacle avoidance, vehicle following, gap- and predecessor determination on curved roads and, using a well-known simulation environment equipped with a physics engine, demonstrate that our emergent platooning approach is effective when vehicle characteristics such as weight, center of mass and friction are considered.