Jun Hao
Driving decision-making, intelligent vehicles, deep learning, pass rate, neural network, reliability
With the popularisation of artificial intelligence, more intelligent products have flooded into various industries. Among them, intelligent transportation and intelligent vehicles have become the main development directions of the automotive industry. Unlike traditional cars, intelligent cars have added driving decision-making functions. Deep learning (DL) neural network algorithms can make judgements on the driving decisions of intelligent vehicles. According to the characteristics of driving decision, an improved DL reinforcement algorithm is proposed based on the DL and reinforcement learning algorithm. In response to the changing environmental factors of driving decisions, a car simulation platform is used to simulate the driving decision changes training in real situations. The experimental results showed that the improved deep reinforcement learning algorithm had a pass rate of 98% in stable road testing. For complex road sections, the passing rate reached 74%. Compared with traditional algorithms, the improved algorithm saved 20% in training time. It has better stability, reliability, generalisation, and real-time performance. The research results indicate that this algorithm has long-term research significance and certain reference value for automotive driving decision-making.
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