Lane Change Decision Algorithm based on Risk Prediction and Fuzzy Logic Method

Mechernene, Amin; Judalet, Vincent; Chaibet, Ahmed; Boukhnifer, Moussa

doi:10.1109/icstcc52150.2021.9607228

Cited by 3 publications

(2 citation statements)

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“…This paper extends a previous work presented in [ 10 ]. We use the global methodology by modifying the final brick (the decision algorithm), which is fuzzy logic, by three other algorithms which have a different philosophy because they are based on learning from naturalistic driving data.…”

Section: Introductionsupporting

confidence: 89%

“…This section is dedicated to the last brick of our decision method, which is the decision-making algorithm, which must decide whether the lane change should be made or not based on the parameters mentioned above: the risks and gain. In a previous article [ 10 ], we already used this methodology with fuzzy logic as a decision algorithm. We compare the results later.…”

Section: Decision Algorithmsmentioning

confidence: 99%

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Detection and Risk Analysis with Lane-Changing Decision Algorithms for Autonomous Vehicles

Mechernene

Judalet

Chaibet

et al. 2022

Sensors

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Despite the great technological advances in ADAS, autonomous driving still faces many challenges. Among them is improving decision-making algorithms so that vehicles can make the right decision inspired by human driving. Not only must these decisions ensure the safety of the car occupants and the other road users, but they have to be understandable by them. This article focuses on decision-making algorithms for autonomous vehicles, specifically for lane changing on highways and sub-urban roads. The challenge to overcome is to develop a decision-making algorithm that combines fidelity to human behavior and that is based on machine learning, with a global structure that allows understanding the behavior of the algorithm and that is not opaque such as black box algorithms. To this end, a three-step decision-making method was developed: trajectory prediction of the surrounding vehicles, risk and gain computation associated with the maneuver and based on the predicted trajectories, and finally decision making. For the decision making, three algorithms: decision tree, random forest, and artificial neural network are proposed and compared based on a naturalistic driving database and a driving simulator.

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Section: Introductionsupporting

confidence: 89%

Section: Decision Algorithmsmentioning

confidence: 99%