Accident reproduction system for the identification of human factors involved on traffic accidents

Siordia, Oscar S.; Diego, Isaac Martín de; Conde, Cristina; Cabello, Enrique

doi:10.1109/ivs.2012.6232126

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…With increasing the usage and importance of vehicle black box recently, several intelligent recording systems with high performance have been presented [1][2][3][4][5]. Kassem et al [1] presented a recorder system developed on a commercial micro controller.…”

Section: Related Wordmentioning

confidence: 99%

“…It determines vehicular situation by several vehicle and road related variables such as speed, brake pedal, rain, seat belt, lights status and 8 push buttons used as collision sensors. In addition, more advanced controller systems [2][3][4][5], which can not only record the main driving data of the car comprehensively and accurately in real-time, but also simulate and reconstruct the accident with data process software or embedded hardware, have been proposed. Consequently people can analyze the accident rapidly and legitimately after a collision.…”

Section: Related Wordmentioning

confidence: 99%

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The architecture for vehicular black-box image communication in VANET

Hee

Park

Hwang

et al. 2014

2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN)

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In this paper, we introduce a new practical and secure architecture for vehicular black-box image communication in a VANET environment. Since black-box images show more detailed descriptions of the vehicle and the ambient conditions, more reliable traffic information and analysis can be provided. The main barrier to establish the black-box image communication system is that the communicational and computational overheads for transferring and processing images are pretty big. In addition, the abuse of black-box images can infringe on the privacy of driver since vehicle's behaviors and driving patterns can be easily exposed from the images. In this paper, we propose an efficient and secure vehicular black-box image communication model. Our proposed model minimizes the communicational and computation overheads by GPS based-key frame extraction and communication technique. In addition, the proposed model, assisted by RSUs, guarantees the reliable use of the images and preserves driver's privacy. We also provide the simulated performance of our model with real black-box images.

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Section: Related Wordmentioning

confidence: 99%

Section: Related Wordmentioning

confidence: 99%

The architecture for vehicular black-box image communication in VANET

Hee

Park

Hwang

et al. 2014

2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN)

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show abstract

“…Most researches related to vehicular black-box [1][2][3][4][5] are focused on developing intelligent recording systems with high performance for detecting and simulating abnormal vehicular situations or events with advanced controllers or diverse vehicular sensors. Related to the black-box image sharing, Hong et al [6] have proposed a black-box evidence collection system, which transmits vehicle's critical video clip to the police station using smart phones for a car accident analysis.…”

Section: Related Workmentioning

confidence: 99%

A group-based vehicular black-box image sharing model using smart phones in VANET

Hee¹,

Kim²,

Do³

et al. 2014

ces

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In this paper, we introduce a new practical and secure architecture for vehicular black-box image sharing in VANET, which consists of black-box equipped vehicles, roadside units (RSUs) and a centralized traffic image control server (TICS). In our model, all vehicles that registered to TICS provide with their black-box images to TICS so that TICS manages all traffic images about the entire roads. Then, the vehicles can obtain up-to-date traffic images for any locations from TICS in real-time. For its efficient and secure use, we propose an RSU-assisted communication mechanism to minimize the communicational overheads, and anonymous authentication protocols for preserving vehicle's privacy. We also analyze the efficiency of our model using NS3 simulator.

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“…Despite the convenience brought by automobile technology, humans also face increasingly serious traffic safety issues. Studies have shown that subjective behaviors such as inattention, unresponsiveness, and impoliteness of drivers toward pedestrians can easily cause unnecessary casualties in traffic accidents, thereby posing a huge threat to human life and property [1][2][3][4][5]. With the substantial improvement of modern control technology and automotive technology, smart cars can assist or even completely replace drivers to perform the main driving operation, thereby providing a solution to traffic safety problems [6,7].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

Cao

Song

Peng

et al. 2020

Sensors

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Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance.

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Accident reproduction system for the identification of human factors involved on traffic accidents

Cited by 6 publications

References 11 publications

The architecture for vehicular black-box image communication in VANET

The architecture for vehicular black-box image communication in VANET

A group-based vehicular black-box image sharing model using smart phones in VANET

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

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