Adaptive Driver Assistance Systems Using LSTM, GRU, Q-Learning, and VARMA for Drowsiness Monitoring, Lane Keeping, and Collision Pre-emptions
Keywords:
Adaptive Driver Assistance Systems, Machine Learning, Deep Learning, Drowsiness Monitoring, Collision Pre-emptionsAbstract
The advancement of Driver Assistance Systems (DAS) has become increasingly crucial in improving vehicle safety and enhancing the driving experience. With the growing number of traffic accidents caused by factors such as drowsiness, improper lane-keeping, and delayed braking, there is a pressing need for more accurate and adaptive systems to aid in driving operations. Existing DAS technologies often suffer from limitations, including inaccurate detection of drowsiness, suboptimal lane-keeping assistance, and inefficient braking mechanisms, leading to a diminished driving experience and compromised safety levels. These limitations have prompted the development of more advanced and precise assistance systems. In this paper, we propose a novel Adaptive Driver Assistance System (ADAS) that leverages the strengths of LSTM, GRU, Q-learning, and VARMA models to address the aforementioned limitations. Our system uses LSTM-based RNNs for accurate drowsiness analysis, GRU-based RNNs for predictive lane keeping, Q-learning for intelligent braking, and VARMA for collision preemption, taking advantage of the respective strengths of these models in time-series prediction, pattern recognition, and decision-making process. The experimental results show that our proposed system significantly improves the performance metrics of the DAS. Specifically, we achieve 8.5% higher precision of drowsiness analysis, 8.3% higher accuracy for drowsiness detection, 4.9% higher precision of lane keeping, 5.5% higher accuracy for intelligent braking, and 4.9% higher precision for collision preemption, when compared with existing models for different scenarios. These improvements highlight the potential of our system in enhancing driving safety and reducing the risk of accidents.
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