Deep learning based predictive models for real time accident prevention in autonomous vehicle networks.
Journal:
Scientific reports
Published Date:
Jul 1, 2025
Abstract
There have been substantial difficulties to road safety brought about by the increasing volume of traffic, which has resulted in the requirement for sophisticated accident prediction and prevention technologies. The use of autonomous vehicle (AV) networks is a potentially useful solution since they make it possible to react in real time to probable collisions. Within the realm of autonomous vehicle networks, this study presents an innovative accident prediction and prevention model that is referred to as A-LAPPM (Attention-based Long- and Short-Term Memory Autoencoder). The purpose of this model is to improve safety. The data received from vehicle sensors, Vehicle-to-Vehicle (V2V) communication, and ambient variables are all incorporated into the model during the process of identifying and responding to potential accident hazards. Critical temporal patterns and danger indicators are captured by the model through the utilization of sequential learning through Long- and Short-Term Memory (LSTM) units and the enhancement of focus through the utilization of an attention mechanism. It is possible to make precise and timely predictions of future mishaps because to this. Extensive experiments are used to assess the usefulness of the A-LAPPM model that has been proposed. These studies evaluate key metrics such as the accuracy of predictions, the response time, the reduction in accident rate, the efficiency of decision-making, and the resilience to false data. According to the findings, the model delivers roughly 11.8% greater prediction accuracy, 28.5% faster response time, and a 50% reduction in accident rates, which ultimately leads to an improvement in the overall performance of autonomous vehicles in scenarios that involve complicated driving situations.
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