Emerging Traffic Data Collection Practices Under Mixed Traffic Conditions: Challenges and Solutions

Budhkar, Anuj Kishor; Asaithambi, Gowri; Maurya, Akhilesh Kumar; Arkatkar, Shriniwas

doi:10.1007/978-981-16-9636-7_6

Cited by 3 publications

(1 citation statement)

References 78 publications

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“…In order to evaluate the proposed method more accurately, we drove through the ROI with a GPS-equipped car and compared the detected speeds with the ones obtained via the GPS (27,28) . Several runs were made at different constant speeds, and the motion data extracted from the vehicle included the time, location, and speed, see Figure 8.…”

Section: • Accuracy Estimationmentioning

confidence: 99%

Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking

Siddiqua,

Saher,

Sumera

2024

IJST

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Objectives: The aim of this study is to develop an efficient and cost-effective solution for predicting vehicle speeds using recorded video data. Methods: The proposed system employs a combination of image processing techniques and computer vision to calibrate cameras for traffic simulation, enabling the extraction of information on average vehicle speeds. It utilizes the Haar Cascade Classifier for object detection, followed by a correlation tracker for vehicle tracking. Speed estimation is achieved through the frame differencing method. The dataset comprises 90 minutes of recorded data from highway cameras, showcasing diverse traffic scenarios with various vehicle types (trucks, trailers, cars, buses, and bikes) at varying speeds. Predicted values are compared with ground truth data obtained from a GPS-equipped car, using Mean Absolute Error (MAE) as the evaluation metric. Findings: The algorithm's performance is evaluated, resulting in an average error rate of 1.72 km/h (2.07%). These findings are compared with state-of-the-art data. Novelty: This study introduces a novel system that combines the Haar Cascade Classifier, correlation tracker, and frame differencing method to track vehicle positions, incorporating bike detection into the analysis, and calculate their moving speeds. A relative analysis underscores the system's performance, emphasizing its effectiveness in real-world applications and demonstrating refinement in accuracy assessment. Keywords: Image processing, Vehicle speed estimation, Haar Cascade Classifier, Correlation tracker, Error rate calculation, Computer vision

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Section: • Accuracy Estimationmentioning

confidence: 99%

Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking

Siddiqua,

Saher,

Sumera

2024

IJST

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An investigation of following behavior and associated safety of MTWs in heterogeneous traffic

Damani

Vedagiri

2023

Transportation Letters

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Collaborative Autonomous Overtaking System for Mixed Traffic Environments

Lamouik,

Ameur,

Yahyaouy

et al. 2024

Transportation Research Record: Journal of the Transportation R

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This paper explores the challenges associated with executing safe overtaking maneuvers. To address these concerns, we introduce a collaborative autonomous overtaking system that leverages Vehicle-to-Vehicle (V2V) communication alongside a camera-based approach for detecting surrounding vehicles. Our approach involves sharing captured images among vehicles to ensure secure overtaking, eliminating blind spots for the overtaking vehicle. The system employs potential field algorithms for path generation and model predictive control to optimize vehicle control throughout the maneuver. Through simulations in various traffic scenarios, we validate the efficacy of the proposed system, showcasing significant improvements in traffic flow and safety, notably enhancing the system safety. This research contributes to the seamless integration of autonomous systems into existing traffic setups by incorporating a camera-based vehicle detection system and reducing reliance on traditional LiDAR and radar technologies.

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Emerging Traffic Data Collection Practices Under Mixed Traffic Conditions: Challenges and Solutions

Cited by 3 publications

References 78 publications

Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking

Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking

An investigation of following behavior and associated safety of MTWs in heterogeneous traffic

Collaborative Autonomous Overtaking System for Mixed Traffic Environments

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