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Abstract-Dynamic collaborative driving involves the motion coordination of multiple vehicles using shared information from vehicles instrumented to perceive their surroundings in order to improve road usage and safety. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal control. Without this capability, higher-level coordination is not possible. This paper focuses on the problem of longitudinal motion control. A detailed nonlinear longitudinal vehicle model which serves as the control system design platform is used to develop a longitudinal adaptive control system based on Monte Carlo Reinforcement Learning. The results of the reinforcement learning phase and the performance of the adaptive control system for a single automobile as well as the performance in a multi-vehicle platoon is presented. I. INTRODUCTIONN major cities throughout the world, urban expansion is leading to an increase of vehicle traffic flow. One solution is to build more roads; another is to automate the process of driving. Dynamic Collaborative Driving is an automated driving approach where multiple vehicles dynamically form groups and networks, sharing information in order to build a dynamic representation of the road to coordinate efficient road travel while maintaining safety.Ultimately our research goal is to create a decentralized control system capable of performing dynamic collaborative driving which is scalable to a large number of vehicles, can be used on any vehicle and in any environment. However, before we can deal with the issue of coordination, basic control of the vehicle must be achieved. The focus of this paper is longitudinal motion control, commonly referred to as adaptive cruise control (ACC). The use of adaptive in ACC is a misnomer as it does not refer to the type of control but is used to indicate that distance control is present in addition to speed control.

Abstract-This paper presents real-time experimental results for a new lane positioning system using Markov localization based on inter-vehicle communication. The proposed system uses low-cost GPS receivers to provide vehicle locations. The system also combines a low-pass Butterworth filter and a particle filter for GPS receiver noise rejection. To study the new lane positioning system, a multi-threaded program in C++, that enables the communication between vehicles and determines their lane positions in real-time, was developed. Experiments using this software validate the effectiveness of the lane positioning system.

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Research in the collaborative driving domain strives to create control systems that coordinate the motion of multiple vehicles in order to navigate traffic both efficiently and safely. In this paper a novel individual vehicle controller based on reinforcement learning is introduced. This controller is capable of both lateral and longitudinal control while driving in a multi-vehicle platoon. The design and development of this controller is discussed in detail and simulation results showing learning progress and performance are presented.