Crossroads --- A Time-Sensitive Autonomous Intersection Management Technique

abstract: For autonomous vehicles, intelligent autonomous intersection management will be required for safe and efficient operation. In order to achieve safe operation despite uncertainties in vehicle trajectory, intersection management techniques must consider a safety buffer around the vehicles. For truly safe operation, an extra buffer space should be added to account for the network and computational delay caused by communication with the Intersection Manager (IM). However, modeling the worst-case computation and network delay as additional buffer around the vehicle degrades the throughput of the intersection. To avoid this problem, AIM, a popular state-of-the-art IM, adopts a query-based approach in which the vehicle requests to enter at a certain arrival time dictated by its current velocity and distance to the intersection, and the IM replies yes/no. Although this solution does not degrade the position uncertainty, it ultimately results in poor intersection throughput. We present Crossroads, a time-sensitive programming method to program the interface of a vehicle and the IM. Without requiring additional buffer to account for the effect of network and computational delay, Crossroads enables efficient intersection management. Test results on a 1/10 scale model of intersection using TRAXXAS RC cars demonstrates that our Crossroads approach obviates the need for large buffers to accommodate for the network and computation delay, and can reduce the average wait time for the vehicles at a single-lane intersection by 24%. To compare Crossroads with previous approaches, we perform extensive Matlab simulations, and find that Crossroads achieves on average 1.62X higher throughput than a simple VT-IM with extra safety buffer, and 1.36X better than AIM.Dissertation/ThesisMasters Thesis Engineering 201

Decentralized Traffic Management: A Synchronization-Based Intersection Control

International audienceControlling the vehicle traffic in large networks remains an important challenge in urban environments and transportation systems. Autonomous vehicles are today considered as a promising approach to deal with traffic control. In this paper, we propose a synchronization-based intersection control mechanism to allow the autonomous vehicle-agents to cross without stopping, i.e., in order to avoid congestions (delays) and energy loss. We decentralize the problem by managing the traffic of each intersection independently from others. We define control agents which are able to synchronize the multiple flows of vehicles in each intersection, by alternating vehicles from both directions. We present experimental results in simulation, which allow to evaluate the approach and to compare it with a traffic light strategy. These results show the important gain in terms of time and energy at an intersection and in a network

Croisement synchronisé de flux de véhicules autonomes dans un réseau

National audienceLes véhicules autonomes sont aujourd'hui considérés comme une approche prometteuse pour le transport des ressources et la régulation du trafic. Dans cet article, nous examinons la possibilité de faire croiser des flux de véhicules autonomes sans les arrêter afin d'éviter les congestions (retards) et la perte d'énergie. Nous proposons un contrôle aux intersections basé sur la synchronisation temporelle des véhicules. Nous décentralisons le problème en gérant indépendamment chaque intersection. Nous définissons un agent de contrôle qui est capable de synchroniser les véhicules arrivant sur une intersection, en assurant l'alternance entre les flux. Nous présentons un simulateur qui permet d'évaluer l'approche et de la comparer avec la stratégie standard des feux de circulation. Les résultats expérimentaux montrent un gain important en termes de temps et d'énergie pour les véhicules à une intersection et dans un réseau

Decentralized Traffic Management: A Synchronization-Based Intersection Control --- Extended Version

Controlling the vehicle traffic in large networks remains an important challenge in urban environments and transportation systems. Autonomous vehicles are today considered as a promising approach to deal with traffic control. In this paper, we propose a synchronization-based intersection control mechanism to allow the autonomous vehicle-agents to cross without stopping, i.e., in order to avoid congestions (delays) and energy loss. We decentralize the problem by managing the traffic of each intersection independently from others. We define control agents which are able to synchronize the multiple flows of vehicles in each intersection, by alternating vehicles from both directions. We present experimental results in simulation, which allow to evaluate the approach and to compare it with a traffic light strategy. These results show the important gain in terms of time and energy at an intersection and in a network.Contrôler le trafic dans les grands réseaux reste un défi important dans les milieux urbains et les systèmes de transport. Les véhicules autonomes sont aujourd'hui considérés comme une approche prometteuse pour fluidifier le trafic. Dans cet article, nous proposons un mécanisme de contrôle d'intersection fondé sur la synchronisation pour permettre aux véhicules-agents autonomes de traverser sans s'arrêter afin d'éviter les congestions (retards) et la perte d'énergie. Nous décentralisons le problème en gérant le trafic de chaque intersection indépendamment des autres. Nous définissons des agents de contrôle qui sont capables de synchroniser les multiples flux de véhicules à chaque intersection, en alternant les véhicules des deux routes. Nous présentons des résultats expérimentaux mesurés en simulation, lesquels permettent d'évaluer l'approche et de la comparer à une stratégie plus classique basée sur les feux de circulation. Ces résultats montrent le gain important en termes de temps et d'énergie à une intersection et dans un réseau

Sliding Window Based Feature Extraction and Traffic Clustering for Green Mobile Cyberphysical Systems

Both the densification of small base stations and the diversity of user activities bring huge challenges for today’s heterogeneous networks, either heavy burdens on base stations or serious energy waste. In order to ensure coverage of the network while reducing the total energy consumption, we adopt a green mobile cyberphysical system (MCPS) to handle this problem. In this paper, we propose a feature extractionmethod using sliding window to extract the distribution feature of mobile user equipment (UE), and a case study is presented to demonstrate that the method is efficacious in reserving the clustering distribution feature. Furthermore, we present traffic clustering analysis to categorize collected traffic distribution samples into a limited set of traffic patterns, where the patterns and corresponding optimized control strategies are used to similar traffic distributions for the rapid control of base station state. Experimental results show that the sliding window is more superior in enabling higher UE coverage over the grid method. Besides, the optimized control strategy obtained from the traffic pattern is capable of achieving a high coverage that can well serve over 98% of all mobile UE for similar traffic distributions

Traffic Control Strategy Formulation and Optimization Enabled by Homogenous Connected and Autonomous Vehicle Systems.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

A Platform for Evaluating Autonomous Intersection Management Policies

There is a significant push towards greater vehicular autonomy on roads to increase convenience and improve overall driver experience. To enable this autonomy, it is imperative that cyber-physical infrastructure be deployed to enable efficient control and communication. An essential component of such road instrumentation is intersection management. This paper develops an intersection management platform that provides the sensing and communication infrastructure needed to enable efficient intersection management policies. The test bed, located in a indoor laboratory, consists of an intersection and multiple robotic vehicles that can sense and communicate. Whereas traditional approaches to intersection management rely on simulations, this test bed enables the first realistic evaluation of several intersection management policies. Six simple but practical centralized and distributed policies are evaluated and compared against the current state of the art, i.e., traffic signals and stop signs. Through extensive experimentation, this paper concludes that, in the scenario tested, even a simple coordinated management policy can halve vehicular delay, while improving the aggregate traversal time of the intersection by 169%