Abstract:

The traffic scheduling problem in time-sensitive networking （TSN） of automotive electrical and electronic architecture is investigated in this paper. To meet practical application requirements， a method for establishing the topology of in-vehicle TSN network is proposed. To address the multi-type traffic scheduling problem in the network， a traffic scheduling strategy based on the Time-Aware Shaper （TAS） mechanism is proposed， and the corresponding mathematical model is established， to reduce the total network delay while considering both the time sensitivity of high-priority traffic and the data integrity of low-priority traffic. To solve the problems of unstable solution efficiency caused by the complex information flow forwarding process in the model and the difficulty of optimization caused by numerous traffic scheduling solutions， an improved genetic algorithm （IGA） is proposed which is optimized from the aspects of setting adaptive crossover probability formula， introducing in taboo search mutation， and combining multiple populations. The experimental results show that the proposed algorithm improves the optimality by 43.47% in end-to-end latency optimization and the solution generation stability by 76.96%. The algorithm can obtain low-latency and high-reliability traffic scheduling solutions for in-vehicle TSN. The research findings of this paper provide insights for the study of intelligent connected vehicles and the optimization of in-vehicle network communication algorithms.

Key words: time-sensitive network（TSN）, traffic scheduling, genetic algorithm, taboo search