Prof. Sébastien Glaser
Research ProfessorCentre for Accident Research & Road Safety – Queensland University of TechnologyLevel 4, K Block | 130 Victoria Park Road - Q 4059 | Kelvin Grove Campus | Australia
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Academic Career Length
25Years (2002-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications15 May 2019 in IEEE Transactions on Intelligent Transportation Systems
Journal Article
LCLaurene Claussmann
Laurene ClaussmannMRMarc Revilloud
Marc Revilloud
DGDominique Gruyer
Dominique Gruyer
Sébastien GlaserSelf-driving vehicles will soon be a reality, as main automotive companies have announced that they will sell their driving automation modes in the 2020s. This technology raises relevant controversies, especially with recent deadly accidents. Nevertheless, autonomous vehicles are still popular and attractive thanks to the improvement they represent to people's way of life (safer and quicker transit, more accessible, comfortable, convenient, efficient, and environment-friendly). This paper presents a review of motion planning techniques over the last decade with a focus on highway planning. In the context of this article, motion planning denotes path generation and decision making. Highway situations limit the problem to high speed and small curvature roads, with specific driver rules, under a constrained environment framework. Lane change, obstacle avoidance, car following, and merging are the situations addressed in this paper. After a brief introduction to the context of autonomous ground vehicles, the detailed conditions for motion planning are described. The main algorithms in motion planning, their features, and their applications to highway driving are reviewed, along with current and future challenges and open issues.
10 May 2010 in IEEE Transactions on Intelligent Transportation Systems
Journal Article
Sébastien GlaserBVBenoit Vanholme
Benoit Vanholme
SMSaïd Mammar
Saïd Mammar
DGDominique Gruyer
Dominique Gruyer
LNLydie Nouveliere
Lydie Nouveliere
This paper presents the design and first test on a simulator of a vehicle trajectory-planning algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The proposed algorithm is designed to run on a fail-safe embedded environment with low computational power, such as an engine control unit, to be implementable in commercial vehicles of the near future. The target platform has a clock frequency of less than 150 MHz, 150 kB RAM of memory, and a 3-MB program memory. The trajectory planning is performed by a two-step algorithm. The first step defines the feasible maneuvers with respect to the environment, aiming at minimizing the risk of a collision. The output of this step is a target group of maneuvers in the longitudinal direction (accelerating or decelerating), in the lateral direction (changing lanes), and in the combination of both directions. The second step is a more detailed evaluation of several possible trajectories within these maneuvers. The trajectories are optimized to additional performance indicators such as travel time, traffic rules, consumption, and comfort. The output of this module is a trajectory in the vehicle frame that represents the recommended vehicle state (position, heading, speed, and acceleration) for the following seconds.
30 May 2006 in IEEE Transactions on Intelligent Transportation Systems
Journal Article
The main goal of this paper is to develop a distance to line crossing (DLC) based computation of time to line crossing (TLC). Different computation methods with increasing complexity are provided. A discussion develops the influence of assumptions generally assumed for approximation. A sensitivity analysis with respect to vehicle parameters and positioning is performed. For TLC computation, both straight and curved vehicle paths are considered. The road curvature being another important variable considered in the proposed computations, an observer for its estimation is then proposed. An evaluation over a digitalized test track is first performed. Real data are then collected through an experiment carried out in test tracks with the equipped prototype vehicle. Based on these real data, TLC is then computed with the theoretically proposed methods. The obtained results outlined the necessity to take into consideration vehicle dynamics to use the TLC as a lane departure indicator.
5 August 2014 in IEEE Transactions on Intelligent Transportation Systems
Journal Article
HVHelene Vorobieva
Helene Vorobieva
Sébastien GlaserNMNicoleta Minoiu-Enache
Nicoleta Minoiu-Enache
SMSaid Mammar
Said Mammar
This paper presents automatic parallel parking for a passenger vehicle, with highlights on a path-planning method and on experimental results. The path-planning method consists of two parts. First, the kinematic model of the vehicle, with corresponding geometry, is used to create a path to park the vehicle in one or more maneuvers if the spot is very narrow. This path is constituted of circle arcs. Second, this path is transformed into a continuous-curvature path using clothoid curves. To execute the generated path, control inputs for steering angle and longitudinal velocity depending on the traveled distance are generated. Therefore, the traveled distance and the vehicle pose during a parking maneuver are estimated. Finally, the parking performance is tested on a prototype vehicle.
26 November 2012 in IEEE Transactions on Intelligent Transportation Systems
Journal Article
BVBenoit Vanholme
Benoit Vanholme
DGDominique Gruyer
Dominique Gruyer
BLBenoit Lusetti
Benoit Lusetti
Sébastien GlaserSMSaïd Mammar
Saïd Mammar
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.
1 June 2008
Conference Paper
JDJ. Dakhlallah
J. Dakhlallah
Sébastien GlaserSMS. Mammar
S. Mammar
YSY. Sebsadji
Y. Sebsadji
Main task in driving safety is the understanding and prevention of risky situations. While looking closer at the accidents data analysis, it appears that vehicle loss of control represents a huge part of car accidents. Preventing such kind of accidents, using assistance systems needs several type of information about vehicle state and vehicle-road interaction phenomenon. Longitudinal velocity, acceleration and yaw rate are easily measured using low cost sensors that are actually mounted in standard on a large part of vehicles. However, other parameters, which have a major impact on vehicle dynamics, are more difficult to measure using vehicle industry technology sensors. These are for example the used friction coefficient and the sideslip angle. Using an appropriate vehicle model and available measurements, the vehicle state as well as the road/tire interaction forces are reconstructed by implementing an extended Kalman filter. Thereafter, we evaluate the used friction coefficient and the sideslip angle estimates. Simulation and estimation results are then compared to real measurements collected by an equipped test vehicle on Satory test track.
About Me
Prof. Sebastien Glaser is a Professor in Intelligent Transportation Systems at CARRS-Q, where he focuses on the safe and sustainable development/deployment of Automated Driving Systems in interaction with other road users (drivers, cyclists, pedestrians, etc.). He obtained his PhD in Automatic and Control in 2004 (defining a driving assistance system in interaction with the driver) and worked as a researcher in the development of Connected and Automated Vehicles (CAV). Since 2009, he has worked across academic and industrial sectors and held senior researcher positions. He led the French ABV project, gathering eight academic and industrial partners, to develop a CAV solution at low speed. He has created, with Dominique Gruyer, CIVITEC, which commercialized the research outputs in virtual environment and simulation (and is now a part of the ESI Group). He was the deputy director and director of LIVIC (a research unit of IFSTTAR, the French Institute of Science and Technology for Transport, spatial planning, development, and Networks) from 2012 to 2015 and project leader of VEDECOM (public–private partnership research institute) between 2015 and 2017, developing the Autonomous Vehicle prototypes. He was involved in French (ANR) and European (FP7 and H2020) initiatives on AV development, testing, and evaluation. He was also leading the technological roadmap task force in France for the AV.