Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning

Rough pavements cause ride discomfort and energy inefficiency for road vehicles. Existing methods to address these problems are time-consuming not adaptive changing driving conditions on rough pavements. With the development of sensor communication technologies, crowdsourced dynamic traffic information become available enhancing performance, particularly addressing issues by controlling speeds. This study proposes a speed control framework pavements, envisioning operation autonomous vehicles based data. We suggest concept ‘maximum comfortable speed’ representing vertical comfort oncoming roads. A deep reinforcement learning (DRL) algorithm is designed learn energy-efficient strategies. The DRL-based model trained using real-world pavement data in Shanghai, China. experimental results show that comfort, efficiency, computation efficiency increase 8.22%, 24.37%, 94.38%, respectively, compared an optimization-based model. indicate proposed effective real-time controls