Real-Time Overtaking Vehicle Detection Based on Optical Flow and Convolutional Neural Network

Wu, Lu-Ting; Tran, Van Luan; Lin, Huei-Yung

doi:10.1007/978-3-030-26633-2_11

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Is not affordable for real-time on-road vehicle detection SURF 36,37 Robust features used in real time Unstable under rotation and illumination changes Optical flow 53,54 Good detection indices Affected by camera movement Remains challenging for slow or temporary stopped vehicle…”

Section: Reduced Computational Costmentioning

confidence: 99%

Vision‐based vehicle detection for road traffic congestion classification

Chetouane

Mabrouk

Jemili

et al. 2020

Concurrency and Computation

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SummaryDue to the increasing number of vehicles in circulation in different urban cities, several automatic traffic monitoring systems have been developed. In particular, traffic monitoring systems using roadside cameras are becoming extensively deployed, as they offer imperative technological advantages compared with other traffic monitoring systems. Vehicle detection and traffic congestion classification are two main steps for video‐based traffic congestion detection systems; the associated methods have a deep impact on the performance of the whole system. In this paper, we investigate four selected vehicle detection methods namely Gaussian Mixture Model (GMM), GMM‐Kalman filter, Optical Flow, and ACF object detector in two contexts: urban and highway. Three traffic congestion classification methods are also studied. The comparative study of the different methods allows us to choose the most appropriate ones to be integrated in the framework proposed to solve the traffic issues in the bridge of Bizerte.

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Section: Reduced Computational Costmentioning

confidence: 99%

Vision‐based vehicle detection for road traffic congestion classification

Chetouane

Mabrouk

Jemili

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

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“…In Wu et al ( 15 ), an overtaking vehicle detection and warning system was proposed that used a rear-mounted monocular camera to capture motion cues combined with convolutional neural network to identify and track vehicles for behavior analysis. In Nishida et al ( 16 ), an image-based system was proposed to detect lane architecture and vehicle presence on the road, to assess the safety of performing an overtake.…”

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confidence: 99%

Model Predictive Control for Full Autonomous Vehicle Overtaking

Lamouik

Yahyaouy

Sabri

2023

Transportation Research Record: Journal of the Transportation R

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Despite the many advancements in traffic safety, vehicle overtaking still poses significant challenges to both human drivers and autonomous vehicles, especially, how to evaluate the safety of passing a leading vehicle efficiently and reliably on a two-lane road. However, few realistic attempts in this field have been made in the literature to provide practical solutions without prior knowledge of the state of the environment and simplifications of vehicle models. These model simplifications make many of the proposed solutions in the literature unusable in real scenarios. Considering the dangers that can arise from performing a defective overtake and the substantial risk of vehicle crashes during high-speed maneuvers, in this paper we propose a system based on model predictive control to accurately estimate the safety of starting a vehicle overtake in addition to vehicle control during the maneuver that aims to ensure a collision-free overtake using a complete and realistic model of the vehicle’s dynamics. The system relies on a stereoscopic vision approach and machine learning techniques (YOLO and DeepSORT) to gather information about the environment such as lane width, lane center, and distance from neighboring vehicles. Furthermore, we propose a set of scenarios to test the performance of the proposed system based on accurate modeling of the environment under a range of traffic conditions and road architecture. The simulation result shows the high performance of the proposed system in ending collisions during overtaking and providing optimal pathing that minimizes travel time.

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