The safety of automated vehicles (AVs) relies on the representation of their environment. Consequently, state-of-the-art AVs employ potent sensor systems to achieve the best possible environment representation at all times. Although these high-performing systems achieve impressive results, they induce significant requirements for the processing capabilities of an AV's computational hardware components and their energy consumption. To enable a dynamic adaptation of such perception systems based on the situational perception requirements, we introduce a model-agnostic method for the scalable employment of single-frame object detection models using frame-dropping in tracking-by-detection systems. We evaluate our approach on the KITTI 3D Tracking Benchmark, showing that significant energy savings can be achieved at acceptable performance degradation, reaching up to 28% reduction of energy consumption at a performance decline of 6.6% in HOTA score.

翻译：自动驾驶车辆（AV）的安全性依赖于其环境表征能力。为此，最先进的自动驾驶车辆采用高性能传感器系统，以始终实现最佳的环境表征。尽管这些高性能系统取得了令人瞩目的成果，但它们对自动驾驶车辆计算硬件组件的处理能力及其能耗提出了重大需求。为了实现基于情境感知需求的感知系统动态自适应，我们提出了一种与模型无关的方法，通过在检测跟踪系统中使用跳帧技术，实现单帧目标检测模型的可扩展部署。我们在KITTI 3D跟踪基准上评估了该方法，结果表明在可接受的性能下降范围内可实现显著的节能效果，即在HOTA评分下降6.6%的情况下，能耗最高可降低28%。