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Bacha Rebiha

dblp:245/3559 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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
Probabilistic and Bayesian machine learning · 50% Autonomous driving · 50%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Autonomous driving › intention prediction
driver intention prediction		 0.412019
A Dynamic Bayesian Network Based Merge Mechanism for Autonomous Vehicles · AAAI 2019
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › bayesian network
dynamic bayesian network		 0.412019
A Dynamic Bayesian Network Based Merge Mechanism for Autonomous Vehicles · AAAI 2019
Smart cities and intelligent transportation
autonomous driving		 0.112019
A Dynamic Bayesian Network Based Merge Mechanism for Autonomous Vehicles · AAAI 2019

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

dynamic bayesian network · 0.8
YearPublicationVenuePosition
2019 A Dynamic Bayesian Network Based Merge Mechanism for Autonomous Vehicles
abstract
This work explores the design of a central collaborative driving strategy between connected cars with the objective of improving road safety in case of highway on-ramp merging scenario. Based on a suitable method for predicting vehicle motion and behavior for a central collaborative strategy, a dynamic Bayesian network method that predicts the intention of drivers in highway on-ramp is proposed. The method was validated using real data of detailed vehicle trajectories on a segment of interstate 80 in Emeryville, California.
Samir Aknine, Bacha Rebiha
 AAAI3