Emergent Behaviors and Traffic Density among Heuristically-Driven Intelligent Vehicles using V2V Communication

International audienceIn this paper, we study the global traffic density and emergent traffic behavior of several hundreds of intelligent vehicles, as a function of V2V communication (for the ego vehicle to perceive traffic) and path-finding heuristics (for the ego vehicle to reach its destination), in urban environments. Ideal/realistic/no V2V communication modes are crossed with straight-line/towards-most-crowded/towards-least-crowded pathfinding heuristics to measure the average trip speed of each vehicle. The behaviours of intelligent vehicles are modelled by a finite state automaton. The V2V communication model is also built based on signal propagation models in an intersection scenario and a Markov-chain based MAC model. Our experiments in simulation over up to 400 vehicles exhibit attractive insights: 1) communication's impact is positive for the performance of the emergent vehicles' behaviour, however, 2) the path-finding heuristics may not obtain their expected collective behaviour due to the communications errors in realistic road environment

Trajectory Planning in a Crossroads for a Fleet of Driverless Vehicles

In a transportation system with fully autonomous vehicles (or cybercars), a lot of tasks have to be executed to answer the demand of a customer to travel between two points. The framework of this paper is an approach consisting in decomposing the planning into three levels, each of which using only a relevant subset of the information, thus reducing the complexity: the macroscopic level, e.g. a city or a region; the mesoscopic level, e.g. a city quarter; the microscopic level, i.e. the surroundings of the cybercar. At the macroscopic level, a path is computed. By path, we mean a succession of edges (road segments) in the graph description of the road network. At the mesoscopic level, paths are transmitted by the upper level and turned into trajectories. A trajectory is a precise space-time curve that the cybercar has to follow. Typical precisions are 10 cm and 1/10 s. The goal of this level is to produce trajectories for all the cybercars circulating in a given area. These trajectories have to be safe, most notably collision-free, but also efficient, i.e

Planung kooperativer Fahrmanöver für kognitive Automobile

Fahrerassistenzsysteme eröffnen die Möglichkeit für automatische Eingriffe in Gefahrensituationen und bieten dadurch ein Potenzial zur Unfallvermeidung und zur Minimierung der Unfallschwere im Straßenverkehr. Die Handlungen mehrerer kognitiver Fahrzeuge können über Funkkommunikation miteinander koordiniert werden. Diese Dissertation untersucht potenziell echtzeitfähige Bewegungsplanungsalgorithmen zur Planung von Fahrmanövern, die von mehreren Fahrzeugen kooperativ ausgeführt werden können