Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Software and Platforms


An environment for autonomous driving decision-making

Keywords: Generic modeling environment - Simulation - Autonomous Cars - Artificial intelligence

Functional Description: The environment is composed of several variants, each of which corresponds to driving scenes: highway, roundabout, intersection, merge, parking, etc. The road network is described by a graph, and is then populated with simulated vehicles. Vehicle kinematics follows a simple Bicycle model, and their behavior is determined by models derived from road traffic simulation literature. The ego-vehicle has access to a description of the scene through several types of observations, and its behavior is controlled through an action space, either discrete (change of lanes, of cruising speed) or continuous ( accelerator pedal, steering wheel angle). The objective function to maximize is also described by the environment and may vary depending on the task to be solved. The interface of the library is inherited from the standard defined by OpenAI Gym, consisting of four main methods: gym.make(id), env.step(action), env.reset(), and env.render().