Driving control based on bilevel optimization and fuzzy logic

Abstract

Driving control in the car‐following (CF) driving behavior has two aspects. First, in what measure an approximation distance is taken as a safe distance guaranteeing the safety of the follower drivers. Second, how to control the follower's vehicle velocities based on the stimulus of the leading vehicle. In this context, to resolve the driving control problem in the CF driving behavior, a bilevel optimization is presented in this paper, based on the behaviors of the follower and leader drivers. Bearing in mind that mathematics has contributed to the imitation of human behaviors, they are now reaching a level of complexity requiring the entry on the scene of a new player, which is artificial intelligence. Thus, in this paper; we used the fuzzy logic theory for modeling a follower driver with a nonnormative behavior. To validate our model, we used a data set from the program of the US Federal Highway Administration. Therefore, according to the experimental results, there is homogeneity between the actual and the simulated travel trajectories in terms of deviation. Besides, the driver's behavior adopted (normative or nonnormative) is reflected in his reactions to the various components of the road.

A Decision Support Model for Transportation Companies to Examine Driver Behavior

“…The acceleration, velocity, and distance between the preceding and host car were considered as parameters that affect the driving style. The same parameters were used in [30], while in [31] sudden acceleration and sudden turns are considered. In [32] authors implemented fuzzy logic to model driving behavior.…”

“…Personality traits and attitudes Yuksel and Atmaca [31] 2020 Sudden acceleration and sudden turn Bennajeh and Ben Said [30] 2021…”

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

“…Artificial intelligence (AI) has altered numerous industries over the past few decades, including healthcare [6,7], manufacturing [8,9], transportation [10,11] and retail [12,13],…”

MIFNet: A lightweight multiscale information fusion network

“…It accurately classifies roads, people, and so on, as shown in Figure 1. Semantic segmentation technology is also widely used in the Internet of Things (IoT), 1 such as: self‐driving, 2 medical imaging, 3,4 robots, 5 and so on. In the IoT environment, it is difficult for mobile terminals 6 or other resource‐constrained edge computing platforms 7 to quickly process semantic segmentation tasks.…”