Deep learning based robust forward collision warning system with range prediction

Venkateswaran, N.; Hans, W. Jino; Padmapriya, N.

doi:10.1007/s11042-021-10703-8

Cited by 7 publications

(1 citation statement)

References 43 publications

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“…The results showed that fusion helps to obtain more accurate position and label information in various prototyping scenarios. Venkateswaran et al [ 86 ] developed a monocular vision-based forward collision warning system (as shown in Figure 7 B), which included three main components: (1) detecting on-road vehicles via a pre-trained YOLO, (2) assigning a unique ID for detected vehicles using a Hungarian algorithm and tracking detected vehicles via Kalman filter, and (3) calculating the distance between the detected vehicle and the ego-vehicle. By testing on different datasets, the system achieved more than 0.85 precision for vehicle detection and less than 9.14 RMSE for vehicle tracking.…”

Section: Analyzing Safety Critical Eventsmentioning

confidence: 99%

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles

Yang,

Ridgeway,

Miller

et al. 2024

Sensors

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Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.

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Section: Analyzing Safety Critical Eventsmentioning

confidence: 99%