Analysis of optimal solutions to robot coordination problems to improve autonomous intersection management policies

International audienceThe deployment of Cooperative Intelligent Transportation Systems (C-ITS) raises the question of future traffic management systems, which will be operating with an increasing amount of information and control over the infrastructure and the vehicles. This topic of research shares some similarities with robot coordination problems, inspiring our research on autonomous intersection management. In this article, we use a mixed-integer linear programming formulation for time-optimal robots coordination along specified paths and apply it to intersection management for autonomous vehicles. Our formulation allows to simultaneously solve a discrete optimal vehicle ordering problem, and a (discretized) continuous optimal velocity planning problem taking into account kinodynamics constraints. This allows faster pruning of the decision tree for the discrete problem, thus reducing computation time. A possible application for ITS is to evaluate the efficiency loss from a given vehicle ordering policy, or dynamically adapt policies to improve their efficiency. Moreover, any intermediary solution found by the solver can be used as a heuristically good policy, with proved bounds on sub-optimality

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