Energy-Efficient Collaborative Perception: A Block-Skipping DNN Approach With Dynamic Frequency Scaling and Environment Awareness

Energy efficiency is crucial for Connected Autonomous Vehicles (CAVs), where real-time perception via Deep Neural Networks (DNNs) demands significant computing resources. Although techniques such as Dynamic Frequency Scaling (DFS) and block-skipping reduce energy usage, they may degrade accuracy or increase inference speed. Integrating these methods with Collaborative Perception (CP), which leverages data sharing among vehicles to improve perception performances, offers a potential trade-off between accuracy and energy usage. This paper introduces EC-PUBSE (Efficient Collaborative Perception Using Block-Skipping, DFS, and Environment awareness), an energy-efficient CP framework for CAVs. EC-PUBSE dynamically allocates computing resources using block-skipping and DFS, coupled with environment-aware response time budget to sustain accuracy and reduce energy. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem, addressing the trade-off between computation energy and accuracy, under communication and response time budget constraints. We leverage model diversity and redundancy to improve system performance compared to standalone and CP. The proposed framework adapts to traffic conditions to offer an adaptable solution for sustainable autonomous driving. Experiments show that EC-PUBSE reduces energy consumption by up to 22%; it improves energy efficiency by up to$2.7\times $and$2.3\times $vs. standalone and collaborative only, respectively, maintaining perception performance in dynamic conditions.