Computer supported estimation of input data for transportation models

Control and management of transportation systems frequently rely on optimization or simulation methods based on a suitable model. Such a model uses optimization or simulation procedures and correct input data. The input data define transportation infrastructure and transportation flows. Data acquisition is a costly process and so an efficient approach is highly desirable. The infrastructure can be recognized from drawn maps using segmentation, thinning and vectorization. The accurate definition of network topology and nodes position is the crucial part of the process. Transportation flows can be analyzed as vehicle’s behavior based on video sequences of typical traffic situations. Resulting information consists of vehicle position, actual speed and acceleration along the road section. Data for individual vehicles are statistically processed and standard vehicle characteristics can be recommended for vehicle generator in simulation models

An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles

Due to the complexity of the natural world, a programmer cannot foresee all possible situations, a connected and autonomous vehicle (CAV) will face during its operation, and hence, CAVs will need to learn to make decisions autonomously. Due to the sensing of its surroundings and information exchanged with other vehicles and road infrastructure, a CAV will have access to large amounts of useful data. While different control algorithms have been proposed for CAVs, the benefits brought about by connectedness of autonomous vehicles to other vehicles and to the infrastructure, and its implications on policy learning has not been investigated in literature. This paper investigates a data driven driving policy learning framework through an agent-based modelling approaches. The contributions of the paper are two-fold. A dynamic programming framework is proposed for in-vehicle policy learning with and without connectivity to neighboring vehicles. The simulation results indicate that while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V) communication of information improves this capability. Furthermore, to overcome the limitations of sensing in a CAV, the paper proposes a novel concept for infrastructure-led policy learning and communication with autonomous vehicles. In infrastructure-led policy learning, road-side infrastructure senses and captures successful vehicle maneuvers and learns an optimal policy from those temporal sequences, and when a vehicle approaches the road-side unit, the policy is communicated to the CAV. Deep-imitation learning methodology is proposed to develop such an infrastructure-led policy learning framework

Hybrid Urban Navigation for Smart Cities

This paper proposes a design for a hybrid, city-wide urban navigation system for moving agents demanding dedicated assistance. The hybrid system combines GPS and vehicle-to-vehicle communication from an ad-hoc network of parked cars, and RFID from fixed infrastructure -such as smart traffic lights- to enable a safely navigable city. Applications for such a system include high-speed drone navigation and directing visually impaired pedestrians.Comment: 21 pages, 10 figures, 2 table

Deep Reinforcement Learning for Resource Allocation in V2V Communications

In this article, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communication systems based on deep reinforcement learning. Each V2V link is considered as an agent, making its own decisions to find optimal sub-band and power level for transmission. Since the proposed method is decentralized, the global information is not required for each agent to make its decisions, hence the transmission overhead is small. From the simulation results, each agent can learn how to satisfy the V2V constraints while minimizing the interference to vehicle-to-infrastructure (V2I) communications

Planning long-term maintenance for electric vehicle charging infrastructure using the Reliability Centered Maintenance (RCM) method

Electric vehicles (EVs) are mainly known for their advantages as emission free, energy efficient and noiseless transport, but electric mobility has never matured in the automotive market and it remains in the shadow of the internal combustion engine (ICE) vehicles. The EV penetration depends so much on the availability of the charging facilities. The availability and the performances of the charging infrastructure will have a major impact on the satisfaction of electric vehicle drivers and therefore on the future viability and successful of the technology. In this context, maintenance will play a key role to ensure appropriate levels of availability and reliability and also to keep the expensive infrastructure in good conditions for a long time: it will need to have a long and trouble free life, if it is to persuade the typical car user to change his behavior and choices. This paper will provide a long-term maintenance plan, in which the preventive maintenance tasks will be defined based on the Reliability Centered Maintenance (RCM) approach, starting from the definition of the electric vehicle charging infrastructure and explaining how it works and by which components it is constituted