Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles

Razzaq, Sheeba; Dar, Amil Roohani; Shah, Munam Ali; Khattak, Hasan Ali; Ahmed, Ejaz; El-Sherbeeny, Ahmed M.; Lee, Seongkwan Mark; Alkhaledi, Khaled; Rauf, Hafiz Tayyab

doi:10.3390/app12031049

Cited by 7 publications

(2 citation statements)

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“…In this context, the probability of death occurring on the road is higher in secondary traffic accidents than in primary ones, making it crucial to quickly identify accidents on the road to prevent subsequent secondary accidents. Consequently, in the field of artificial intelligence, technologies are being actively developed to quickly detect traffic accidents or accurately classify types of accidents [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Cross-Modality Interaction-Based Traffic Accident Classification

Oh,

Ban

2024

Applied Sciences

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Traffic accidents on the road lead to serious personal and material damage. Furthermore, preventing secondary accidents caused by traffic accidents is crucial. As various technologies for detecting traffic accidents in videos using deep learning are being researched, this paper proposes a method to classify accident videos based on a video highlight detection network. To utilize video highlight detection for traffic accident classification, we generate information using the existing traffic accident videos. Moreover, we introduce the Car Crash Highlights Dataset (CCHD). This dataset contains a variety of weather conditions, such as snow, rain, and clear skies, as well as multiple types of traffic accidents. We compare and analyze the performance of various video highlight detection networks in traffic accident detection, thereby presenting an efficient video feature extraction method according to the accident and the optimal video highlight detection network. For the first time, we have applied video highlight detection networks to the task of traffic accident classification. In the task, the most superior video highlight detection network achieves a classification performance of up to 79.26% when using video, audio, and text as inputs, compared to using video and text alone. Moreover, we elaborated the analysis of our approach in the aspects of cross-modality interaction, self-attention and cross-attention, feature extraction, and negative loss.

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Section: Introductionmentioning

confidence: 99%

Cross-Modality Interaction-Based Traffic Accident Classification

Oh,

Ban

2024

Applied Sciences

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“…This is all the more true given that Kalra and Paddock [6] have demonstrated that the validation of AV by driving would require covering billions of kilometers. The use of simulation has many advantages, including testing controllability and path planning strategies, simulating different ranges of operational parameters, ensuring reproducibility and efficiency of the tests [7,8], testing specific collision avoidance strategies [9], etc. Previous authors have worked on the validation of automotive models and propose an assessment process that is not only focused on safety validation, but also on the quantification of modeling errors and uncertainties of the simulation compared to real driving [10].…”

Section: Introductionmentioning

confidence: 99%

An Approach to Guide the Search for Potentially Hazardous Scenarios for Autonomous Vehicle Safety Validation

et al. 2023

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Safety validation of Autonomous Vehicles (AV) requires simulation. Automotive manufacturers need to generate scenarios used during this simulation-based validation process. Several approaches have been proposed to master scenario generation. However, none have proposed a method to measure the potential hazardousness of the scenarios with regard to the performance limitations of AV. In other words, there is no method offering a metric to guide the search for potentially critical scenarios within the infinite space of scenarios. However, designers have knowledge of the functional limitations of AV components depending on the situations encountered. The more sensitive the AV is to a situation, the more safety experts consider it to be critical. In this paper, we present a new method to help estimate the sensitivity of AV to logical situations and events before their use for the generation of concrete scenarios submitted to simulators. We propose a characterization of the inputs used for sensitivity analysis (definition of the context of the automation function, generation of functional and logical situations with their associated events). We then propose an approach to set up a distribution function that will make it possible to select situations and events according to their importance in terms of sensitivity. We illustrate this approach by implementing it on the Traffic Jam Chauffeur (TJC) function. Finally, we compare the obtained sensitivity rank with expert judgment to demonstrate its relevance. This approach has been shown to be a promising method to guide the search for potentially hazardous scenarios that are relevant to the simulation-based safety validation process for AV.

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