Energy-Efficient Autonomous Driving with Adaptive Perception and Robust Decision

Abstract--Autonomous driving is an emerging technology that is expected to bring significant social, economic, and environmental benefits. However, these benefits come with rising energy consumption by computation engines, limiting the driving range of vehicles, especially electric ones. Perception computing is typically the most power-intensive component, as it relies on large-scale deep learning models to extract environmental features. Recently, numerous studies have employed model compression techniques, such as sparsification, quantization, and distillation, to reduce computational consumption. However, these methods often result in either a substantial model size or a significant drop in perception accuracy compared to high-computation models. T o address these challenges, we propose an energy-efficient autonomous driving framework, called EneAD, which includes an adaptive perception and a robust decision module. In the adaptive perception module, a perception optimization strategy is designed from the perspective of data management and tuning. Firstly, we manage multiple perception models with different computational consumption and adjust the execution framerate dynamically. Then, we define them as knobs and design a transferable tuning method based on Bayesian optimization to identify promising knob values that achieve low computation while maintaining desired accuracy. T o adaptively switch the knob values in various traffic scenarios, a lightweight classification model is proposed to distinguish the perception difficulty in different scenarios. In the robust decision module, we propose a decision model based on reinforcement learning and design a regularization term to enhance driving stability in the face of perturbed perception results. EneAD can reduce perception consumption by 1.9 to 3.5 and thus improve driving range by 3.9% to 8.5%. Autonomous driving has gained broad attention from the public during the last few years [1], [2]. With intelligence, the autonomous vehicle can have a more comprehensive perception of the surrounding traffic environment and make more reasonable driving decisions compared to human drivers. As a result, it is expected to bring society a large number of benefits, including improved mobility and a significant reduction in collisions. For example, the computing platform using the Nvidia AGX Orin SoC [4] has a Thermal Design Power (TDP) of 800W . These power demands can also increase the thermal demands on a vehicle's climate-control system.