Semi-autonomous Intersection Collision Avoidance through Job-shop Scheduling

In this paper, we design a supervisor to prevent vehicle collisions at intersections. An intersection is modeled as an area containing multiple conflict points where vehicle paths cross in the future. At every time step, the supervisor determines whether there will be more than one vehicle in the vicinity of a conflict point at the same time. If there is, then an impending collision is detected, and the supervisor overrides the drivers to avoid collision. A major challenge in the design of a supervisor as opposed to an autonomous vehicle controller is to verify whether future collisions will occur based on the current drivers choices. This verification problem is particularly hard due to the large number of vehicles often involved in intersection collision, to the multitude of conflict points, and to the vehicles dynamics. In order to solve the verification problem, we translate the problem to a job-shop scheduling problem that yields equivalent answers. The job-shop scheduling problem can, in turn, be transformed into a mixed-integer linear program when the vehicle dynamics are first-order dynamics, and can thus be solved by using a commercial solver.Comment: Submitted to Hybrid Systems: Computation and Control (HSCC) 201

An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected Intersections

In this paper, we provide a decentralized optimal control framework for coordinating connected and automated vehicles (CAVs) in two interconnected intersections. We formulate a control problem and provide a solution that can be implemented in real time. The solution yields the optimal acceleration/deceleration of each CAV under the safety constraint at "conflict zones," where there is a chance of potential collision. Our objective is to minimize travel time for each CAV. If no such solution exists, then each CAV solves an energy-optimal control problem. We evaluate the effectiveness of the efficiency of the proposed framework through simulation.Comment: 8 pages, 5 figures, IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS 201

Efficient management of road intersections for automated vehicles – The FRFP system applied to the various types of intersections and roundabouts.

In the last decade, automatic driving systems for vehicles circulating on public roads have become increasingly closer to reality. There is always a strong interest in this topic among research centers and car manufacturers. One of the most critical aspects is the management of intersections, i.e., who will have to go first and in what ways? This is the question we want to answer through this research. Clearly, the goal is to manage the intersection safely, making it possible to reduce road congestion, travel time, emissions, and fuel consumption as much as possible. The research is conducted by comparing a new management system with the systems already known in the state of the art for different types of intersections. The new system proposed by us is called FRFP (first to reach the end of the intersection first to pass). In particular, vehicles will increase or decrease their speed in collaboration with each other by making the right decision. The vehicle that can potentially reach the intersection exit first

Connected and Automated Vehicle Enabled Traffic Intersection Control with Reinforcement Learning

Recent advancements in vehicle automation have led to a proliferation of studies in traffic control strategies for the next generation of land vehicles. Current traffic signal based intersection control methods have significant limitations on dealing with rapidly evolving mobility, connectivity and social challenges. Figures for Europe over the period 2007-16 show that 20% of road accidents that have fatalities occur at intersections. Connected and Automated Mobility (CAM) presents a new paradigm for the integration of radically different traffic control methods into cities and towns for increased travel time efficiency and safety. Vehicle-to-Everything (V2X) connectivity between Intelligent Transportation System (ITS) users will make a significant contribution to transforming the current signalised traffic control systems into a more cooperative and reactive control system. This research work proposes a disruptive unsignalised traffic control method using a Reinforcement Learning (RL) algorithm to determine vehicle priorities at intersections and to schedule their crossing with the objectives of reducing congestion and increasing safety. Unlike heuristic rule-based methods, RL agents can learn the complex non-linear relationship between the elements that play a key role in traffic flow, from which an optimal control policy can be obtained. This work also focuses on the data requirements that inform Vehicle-to-Infrastructure (V2I) communication needs of such a system. The proposed traffic control method has been validated on a state-of-the-art simulation tool and a comparison of results with a traditional signalised control method indicated an up to 84% and 41% improvement in terms of reducing vehicle delay times and reducing fuel consumption respectively. In addition to computer simulations, practical experiments have also been conducted on a scaled road network with a single intersection and multiple scaled Connected and Automated Vehicles (CAV) to further validate the proposed control system in a representative but cost-effective setup. A strong correlation has been found between the computer simulation and practical experiment results. The outcome of this research work provides important insights into enabling cooperation between vehicles and traffic infrastructure via V2I communications, and integration of RL algorithms into a safety-critical control system