Predicting and Preventing Fatal Crashes

Pandey, Kavita; Fulzele, Prasenjeet; Singh, Rajat Vikram; Kumar, Prasant; Singh, Saransh

doi:10.1109/ic3.2019.8844886

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Hence, to reduce the congestion level of traffic, the methodology mandatorily needed to predict the traffic jams. Prognosticating the prevalence of crashes that pertains to the count of crashes jotted down for a unit of time at a concrete location is benignant in monitoring highways [26,27]. Evading auguring collisions will have high strike on reducing road concussion [28].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

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Section: Introductionmentioning

confidence: 99%

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The development appears cumulative since more and more features of the driving task are automated in new cars [2]. One primary reason why AVs are of value is the possibility of eliminating human driving errors, which account for 93% of road accidents [3], [4]. Furthermore, AVs are safer because they are faster and more accurate in driving tasks as well as in the detection of objects and events [5]- [8].…”

mentioning

confidence: 99%

Talking Cars, Doubtful Users—A Population Study in Virtual Reality

Derakhshan

Nezami

Wachter

et al. 2022

IEEE Trans. Human-Mach. Syst.

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Autonomous vehicles represent a significant development in our society, and their acceptance will largely depend on trust. This study investigates strategies to increase trust and acceptance by making the cars' decisions. For this purpose, we created a virtual reality (VR) experiment with a self-explaining autonomous car, providing participants with verbal cues about crucial traffic decisions. First, we investigated attitudes toward self-driving cars among 7850 participants using a simplified version of the Technology Acceptance Model (TAM) questionnaire. Results revealed that female participants are less accepting than male participants, and that there is a general decline among all genders. Otherwise in general, a self-explaining car has a positive impact on trust and perceived usefulness. Surprisingly, it adversely affected the intention to use and perceived ease of use. This entails dissociation of trust from the other items of the questionnaire. Second, we analyzed behavioral of 26 750 participants to investigate the effect of self-explaining systems on head movements during the VR drive. We observed significant differences in head movements during the

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Predicting and Preventing Fatal Crashes

Cited by 2 publications

References 13 publications

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Talking Cars, Doubtful Users—A Population Study in Virtual Reality

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