Thierry Maurin

Real-time Bounded-error State Estimation for Vehicle Tracking

Estimating the configuration of a vehicle is crucial for its navigation. Most approaches are based on (extended) Kalman filtering or particle filtering. An attractive alternative is considered here, which relies on interval analysis. Contrary to classical extended Kalman filtering it allows global localization, and contrary to particle filtering it provides guaranteed results in the sense that a set is computed that contains all of the configurations that are consistent with the data and hypotheses. This paper presents a real-time implementation of the process including a description of the platform and its modeling, the integration of the errors on the model and the localization method itself.

Experimental vehicle localization by bounded-error state estimation using interval analysis

Estimating the configuration of a vehicle is crucial for navigation. The most classical approaches are Kalman filtering and Bayesian localization, often implemented via particle filtering. This paper reports on-going experimentation with an attractive alternative approach recently developed and based on interval analysis. Contrary to classical extended Kalman filtering, this approach allows global localization, and contrary to Bayesian localization it provides guaranteed results in the sense that a set is computed that contains all of the configurations that are consistent with the data and hypotheses. The approach is particularly robust to outliers.

PICAR: experimental platform for road tracking applications

The PICAR platform is an electrical car including an embedded electronics system. The generic goal is to design an embedded multi sensor plat-form for automotive application such as collision avoidance. Therefore the system includes classical sensors like video camera and ultrasonic sensors, a PC bi-processors, a CAN network and dedicated software for signal and image processing, data fusion and decision system. This plat-form allows experimenting customized sensors and specific architectures dedicated to fusion systems and data processing. The target scenarios are collision avoidance system, automatic parking, and lateral control application on a road lane.

Autonomous parking carrier for intelligent vehicle

In this paper, we consider a parking method for autonomous vehicle in an underground car park. The implemented method is decomposed into three tasks. Starting from configuration given by the vehicle owner, the first one is the motion control of the vehicle from his residence to the car park. After joining the underground car park thanks to the implementation of a path tracking method, the next step is the scrutation of a free space in the car park followed by the operations used for the parking maneuver. The last one, during all the previously seen stages, is the localization mechanisms that allows the vehicle to keep a correct position in the global frame during the whole displacement.

Hybrid neural-based decision level fusion architecture: application to road traffic collision avoidance

A hybrid decision level architecture for a road collision risks avoidance system is presented. The goal of the decision level is to clas- sify the behavior of the vehicles observed by a smart system or vehicle. The knowledge of vehicle behavior enables the best management of the smart system resources. The association of a model to each observed vehicle mainly enables the limitation of inference and of the set of actions to be activated; thus the interactions between system levels can be more intelligent. The decision level of this architecture is composed of a neural classifier, which is associated to a numerical classifier. Each of these classifiers provides decisions that are expressed within the framework of fuzzy theory. An optimal fusion policy is reached using the functional neural network tool.

Basis for Intelligent Interactions

Abstract A key aspect in the field of multi-agents systems concerns the interaction abilities of the agents. The situated agents are required to be closely related to their environment in order to adapt their behaviour to the dynamics and carry out efficient interactions with their acquaintances. The behaviour expected from these agents supposes a certain ability to focalize on the relevant data issued from their environment. This paper presents the “characterization” scheme that is our basis towards the design of agents with focalization skills.

Reconstruction of 2D parameters using infrared sensors

This paper presents an algorithm used for 2-D parameters estimation, it takes into account the low amount of data provided by dedicated infrared sensors. We use fuzzy modelization to cope with reconstruction uncertainty. The concept of entropy adapted to fuzzy sets is used as a decision criterion to provide a best regularized solution for the ill-posed problem of the reconstruction of 2-D parameters.

On board data fusion and decision system used for obstacle detection: a network and a real time approach

The paper describes a system and an algorithm used for multi-sensorial data fusion. The primary goal is to meet real time constraints with perspectives of low costs products. So the authors have chosen to use binary sensors that supply a relatively low amount of data, which allows the implementation of fast algorithms in order to compute a 2-D representation of a vehicle environment on a one DSP board system. The original parts of this work are located in the definition of system architecture (amount of data dispatching, asynchronism managing and processes localization), and in the implementation of a sub-optimal Kalman like algorithm (with classification rules before decision). the whole system part of a vehicle avoidance demonstrator, and is organized around a PC/AT bus.<<ETX>>

FORMAL MODEL FOR ROAD TRAFFIC COLLISION RISK AVOIDANCE

Abstract An on-board collision avoidance system contains a reasoning level which integrates numerical and symbolic information and returns accordingly the adequate deliberate response. The study focuses on the primitives that are required by both the analysis and the decision parts of the reasoning process. Some insight is gained in the symbolic reasoning process with the definition of a numerical expressive representation of the universe of discourses dynamics. This study is based on the possibility theory and uses graph modelling.

Control and validation of expert tasks for a collision avoidance system

In order to improve car driving security, there have been a lot of developments by car constructors: air bag, seat belt. All of them are made to reduce human physical damage in case of accident but do not prevent the accident from taking place. We describe our work which forms part of the European project Prometheus which aims to propose a real time collision avoidance system embedded in a car. In this paper we discuss particularly the decision module which warns the driver against an eventual collision. For this we have chosen to use an expert system.