Car-driving assistance using organization measurement of reactive multi-agent system

Abstract

This work presents an approach to the obstacle avoidance problem, applicable in the frame of driver assistance. A decision, expressed as a proposed acceleration vector for the vehicle, is elaborated from the evaluation of a set of indicators characterizing the global state of a system of reactive agents (RMAS). Those agents evolve in a virtual environment produced on the base of vehicle's perceptions of the material environment around it. Agent-to-agent and agent-to-environment interactions are defined in order to produce a distribution of agents over the virtual environment. This distribution, taken as the global state of the system, is analyzed by applying a set of indicators inspired from statistical physics, to calculate a new vehicle's acceleration vector. This work presents the details of the RMAS model and its interaction laws, together with the global state evaluation functions. The approach has been applied to experimentation with a laboratory vehicle. Some experimental results are presented here.

EVLibSim: A tool for the simulation of electric vehicles’ charging stations using the EVLib library

“…In a slightly different domain, VIVUS (Virtual Intelligent Vehicle Urban Simulator) [15] simulates vehicles and sensors, taking into consideration their physical structure, while applying artificial intelligence algorithms such as platoon solutions [13] and obstacle avoidance techniques [16]. The urban vehicle simulator is based on two main engines: one for physics simulation and one for 3D immersion in a topological environment.…”

“…Then the availability of charger and energy to start the charging immediately is checked (lines 9-12). If both charger and energy exist, then the price to pay for the charging p j is calculated and the event state is set to accepted (lines [13][14][15][16][17]. If no charger and/or no energy is available for immediate charging, then the scheduling of the event for later charging is considered.…”

Sphericall: A Human/Artificial Intelligence interaction experience

“…In many cases, multi-agent based approaches exhibit effectiveness in various fields such as life simulation [24], crowd simulation, robots cooperation [25] or vehicle control related to devices such as obstacle avoidance systems. The multi-agent systems design generally focuses on agents' definition (internal states, perception and behaviour,...) and/or on the interactions between agents and their environment using biological [19], [26], [27], [28] or physical inspiration sources [29], [30], [31]. One can find two main trends in multi-agent design: the cognitive and the reactive approaches.…”

Vehicle platoon and obstacle avoidance: a reactive agent approach

“…Concerning obstacle avoidance, we proposed an approach belonging to the reactive navigation class [19,20]. Concerning platoon control, the proposed approach is local: the behaviour of each vehicle agent bases exclusively on local perceptions.…”