Simulateur de conduite

Résumé

Le terme simulateur de conduite désigne en général un simulateur de véhicule terrestre comme un véhicule automobile, un véhicule ferroviaire ou même celui d'un navire. C'est un simulateur piloté dans lequel un conducteur réel est capable de conduire un véhicule virtuel. Contrairement à ce que l'on peut imaginer, un simulateur de conduite terrestre est plus complexe qu'un simulateur de vol, entre autres pour les raisons suivantes :

la conduite au sol se fait essentiellement par la vision des détails de la scène extérieure et en interaction avec tous les objets et mobiles proches : la scène extérieure est essentielle et le trafic alentour doit apparaître comme réactif aux actions du conducteur alors que dans un aéronef seules les phases de décollage et d'atterrissage nécessitent une vue extérieure précise,
un véhicule terrestre est soumis à des mouvements, changements d'accélération et variations de vitesse moins amples mais plus fréquents qu'un aéronef,
le conducteur conduit en

Source officielle

https://fr.wikipedia.org/wiki/Simulateur_de_conduite

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (17)

Publications associées

Chargement

Personnes associées (5)

Personnes associées

Chargement

Unités associées (3)

Unités associées

Chargement

Concepts associés (2)

Concepts associés

Chargement

Cours associés (1)

The goal of VR is to embed the users in a potentially complex virtual environment while ensuring that they are able to react as if this environment were real. The course provides a human perception-action background and describes the key programming techniques for achieving efficient VR applications

Cours associés

Chargement

Séances de cours associées (3)

Séances de cours associées

Chargement

Distributed Graph-Based Control of Convoys of Heterogeneous Vehicles using Curvilinear Road Coordinates

Alcherio Martinoli, Inaki Navarro Oiza, Milos Vasic, Florian Zimmermann

This paper investigates the problem of controlling a heterogeneous group of vehicles with the aim of forming multi-lane convoys. We use a distributed, graph-based control law, implemented in a longitudinal coordinate system parallel to the road. Each vehicle maintains a local graph with information from only nearby vehicles, in which the desired distances between vehicles are calculated dynamically. This allows for fast adaptation to the changes in the number of vehicles and their positions. We have also implemented a distributed mechanism that allows vehicles to change lane in a cooperative way within the convoy. Systematic experiments have been carried out in a high-fidelity simulator in order to show the performance of the proposed control law.

Ieee2016

Chargement

Cooperative Multiple Dynamic Object Tracking on Moving Vehicles Based on Sequential Monte Carlo Probability Hypothesis Density Filter

Jonathan Gan, Alcherio Martinoli, Milos Vasic

This paper proposes a generalized method for tracking of multiple objects from moving, cooperative vehicles -- bringing together an Unscented Kalman Filter for vehicle localization and extending a Sequential Monte Carlo Probability Hypothesis Density filter with a novel cooperative fusion algorithm for tracking. The latter ensures that the fusion of information from cooperating vehicles is not limited to a fully overlapping Field Of View (FOV), as usually assumed in popular distributed fusion literature, but also allows for a perceptual extension corresponding to the union of the vehicles' FOV. Our method hence allows for an overall extended perception range for all cooperative vehicles involved, while preserving same or improving the accuracy in the overlapping FOV. This method also successfully mitigates noisy sensor measurement and clutter, as well as localization inaccuracies of tracking vehicles using Global Navigation Satellite Systems (GNSS). Finally, we extensively evaluate our method using a high-fidelity simulator for vehicles of varying speed and trajectories.

Ieee2016

Chargement

A Collaborative Sensor Fusion Algorithm for Multi-Object Tracking Using a Gaussian Mixture Probability Hypothesis Density Filter

Alcherio Martinoli, Milos Vasic

This paper presents a method for collaborative tracking of multiple vehicles that extends a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with a collaborative fusion algorithm. Measurements are preprocessed in a detect-before-track fashion, and cars are tracked using a rectangular shape model. The proposed method successfully mitigates clutter and occlusion problems. In order to extend the field of view of individual vehicles and increase the estimation confidence in the areas where a target is observable by multiple vehicles, PHD intensities are exchanged between vehicles and fused in the Collaborative GM-PHD filter using a novel algorithm based on the Generalized Covariance Intersection. The method is extensively evaluated using a calibrated, high-fidelity simulator in scenarios where vehicles exhibit both straight and curved motion at different speeds.

2015

Chargement