Benoit Lusetti’s research while affiliated with Institut Lavoisier de Versailles and other places

As automated vehicles are getting closer to becoming a reality, it will become mandatory to be able to characterise the performance of their obstacle detection systems. This validation process requires large amounts of ground-truth data, which is currently generated by manually annotation. In this paper, we propose a novel methodology to generate ground-truth kinematics datasets for specific objects in real-world scenes. Our procedure requires no annotation whatsoever, human intervention being limited to sensors calibration. We present the recording platform which was exploited to acquire the reference data and a detailed and thorough analytical study of the propagation of errors in our procedure. This allows us to provide detailed precision metrics for each and every data item in our datasets. Finally some visualisations of the acquired data are given.

Map-matching GPS traces on a reference road network often increases point positional accu- racy, especially in urban environment where satellite signal is frequently obstructed by buildings. However, with receivers improvement, road data errors may no longer be ipso facto considered as negligible in front of GPS trace errors. This raises the question of sensitivity of map-matching output precision to input network geometric quality. We address the problem by attempting to relate gain in precision with a network quality index, based on two commonly used measures : averaged Hausdorff distance and areal difference. Analysis has been conducted by performing both GPS traces simulation and network perturbation. Results highlighted that the areal dif- ference (for which we demonstrate that it can be considered as a distance) seems to be better related to map-matching output errors. We also provide an upper bound on the impact of using a surrogate reference network and we apply it to a practical case study.

Automated vehicles and Advanced Driver Assistance Systems (ADAS) face a variety of complex situations that are dealt with numerous sensors for the perception of the local driving area. Going forward, we see an increasing use of multiple, different sensors inputs with radar, camera and inertial measurement the most common sensor types. Each system has its own purpose and either displays information or performs an activity without consideration for any other ADAS systems, which does not make the best use of the systems. This paper presents an embedded real-time system to combine the attributes of obstacles, roadway and ego-vehicle features in order to build a collaborative local map. This embedded architecture is called PerSEE: a library of vision-based state-of-the-art algorithms was implemented and distributed in processors of a main fusion electronic board and on smart-cameras board. The embedded hardware architecture of the full PerSEE platform is detailed, with block diagrams to illustrate the partition of the algorithm on the different processors and electronic boards. The communications interfaces as well as the development environment are described.

This paper presents the design and practical implementation of a lane departure avoidance assistance for passenger vehicles based on a state feedback dynamic controller. The road curvature is taken into account as an internal model to ensure convergence of the lateral offset to zero at steady state, even on curvy roads. Lyapunov theory and bilinear matrix inequalities including bounds in the control input and constraints for poles clustering are used to minimise the reachable set of the vehicle after activation of the assistance. The proposed control strategy is simulated in CarSim environment and successfully tested on a prototype vehicle.

This article presents the design of a lane departure avoidance system which is conceived to operate even in demanding manoeuvres with respect to the lateral vehicle dynamics. Piecewise affine state feedback and output feedback controllers are used to handle the nonlinear behaviour of the lateral tyre forces. The controllers are designed based on the search of a piecewise quadratic Lyapunov function casted as a bilinear matrix inequalities problem. Experimental tests demonstrate the performance of the controller in degraded road conditions.

This paper presents a vehicle trajectory controller for an automated vehicle with mechanical steering system, capable of continuously switching from fully automated to human controlled driving. When the driver wishes to correct the controller trajectory, the parameters of the P.I. based controller are adapted to ease the maneuver. The driver is kept informed on the planned trajectory via an incentive torque on the driving wheel. Implementation on test vehicle shows that the controller guaranties a rapid and accurate control in fully automated driving mode even on sharp curvature roads, and a quick and safe control transfer to the human when requested.

Simulation has been widely used to estimate the benefits of ADAS with embedded sensors or more recently Cooperative Systems based on Inter-Vehicular Communications. This paper presents the proposal of a new architecture built with the both SiVIC and RTMaps platforms in order to prototype, to test and to validate eco-driving applications. In this architecture, the innovation is mainly due to the real-time immersion of a real driver in a 3D virtual environment. In this “Hardware In the Loop” platform, major contributions have been made about vehicle modeling updating, interconnection between SiVIC and an HMD. Moreover, a first modeling of a consumption sensor is proposed and used in order to achieve some tests of fuel consumption on the virtual Satory's track. All these contributions have been tuned with on-road measurements to improve reality of the scenarios. We discuss the results of a simple eco-driving scenario implemented to validate our architecture's capabilities.

This paper discusses driving system design based on traffic rules. This allows fully automated driving in an environment with human drivers, without necessarily changing equipment on other vehicles or infrastructure. It also facilitates cooperation between the driving system and the host driver during highly automated driving. The concept, referred to as legal safety, is illustrated for highly automated driving on highways with distance keeping, intelligent speed adaptation, and lane-changing functionalities. Requirements by legal safety on perception and control components are discussed. This paper presents the actual design of a legal safety decision component, which predicts object trajectories and calculates optimal subject trajectories. System implementation on automotive electronic control units and results on vehicle and simulator are discussed.