Low-Carbon Autonomous Driving Computing via Adaptive Solar Batteries

Autonomous driving (AD) systems demand high computational power, leading to substantial carbon emissions from power usage. Beyond emissions related to power usage, embodied carbon from car-borne battery manufacturing is also a major concern. Repeated battery charging cycles accelerate capacity degradation, shorten battery lifespan, and ultimately necessitate battery replacement, which results in additional embodied carbon emissions. Moreover, battery degradation is affected by environmental factors such as ambient temperature and charging current, making it difficult to predict. To reduce both power usage and embodied carbon emissions, we propose LCAD, a low-carbon autonomous driving computing system via adaptive solar battery systems. LCAD harvests solar energy to reduce carbon emissions from fossil power usage. To mitigate embodied carbon from battery degradation, it dynamically switches the car-borne battery between solar charging and non-charging options. However, achieving optimal switching is challenging due to intricate interdependencies among factors such as temperature variations and fluctuating solar energy availability. To this end, we employ deep reinforcement learning (DRL) to learn an optimal switching policy. Real-world tracedriven simulation results demonstrate that LCAD significantly reduces overall carbon emissions compared with the conventional design without solar energy and battery and the other battery charging control approaches.