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Salim Srairi

dblp:56/7734 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 50% Autonomous driving · 25% Robot navigation and mapping · 25%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
robot control		 0.112009
Rollover risk prediction of an instrumented heavy vehicle using high order sliding mode observer · ICRA 2009
Robotics › Motion planning and robot control › nonlinear observer
sliding mode observer		 0.112009
Rollover risk prediction of an instrumented heavy vehicle using high order sliding mode observer · ICRA 2009
Robotics › Robot navigation and mapping
state estimation		 0.112009
Rollover risk prediction of an instrumented heavy vehicle using high order sliding mode observer · ICRA 2009
Robotics › Autonomous driving › vehicle control
vehicle dynamics control		 0.112009
Rollover risk prediction of an instrumented heavy vehicle using high order sliding mode observer · ICRA 2009
Smart cities and intelligent transportation
intelligent vehicles		 0.012009
Rollover risk prediction of an instrumented heavy vehicle using high order sliding mode observer · ICRA 2009

Methods — techniques the papers use, named apart from their topics

load transfer ratio estimation · 0.2high order sliding mode observer · 0.2
YearPublicationVenuePosition
2009 Rollover risk prediction of an instrumented heavy vehicle using high order sliding mode observer
abstract
In this paper, an original method about heavy vehicles rollover risk prediction is presented and validated experimentally. It is based on the calculation of the LTR (load transfer ratio) which depends on the estimated vertical forces using high order sliding mode observers. The validation tests were carried out on an instrumented truck rolling on the road at various speeds and lane-change manoeuvres. Many scenarios have been experienced: driving on straight line, curve line and zigzag to emphasize the rollover phenomenon and its prediction to set off an alarm to the driver.
Hocine Imine, Abdelaziz Benallegue, Tarek Madani, Salim Srairi
 ICRA4
2002 Design of 42 V brushless DC motor for electric fan
Salim Srairi, Christophe Espanet, Thierry Pera, J.-E. Torlay, Abdellatif Miraoui, Jean Marie Kauffmann
 VTC Spring1