TinyCenterSpeed让小型赛车实现实时多目标检测与追踪。
TinyCenterSpeed: Efficient Center-Based Object Detection for Autonomous Racing
- 基于CenterPoint改进，结合TPU实现轻量化实时推理。
- 检测精度和速度估计提升超61%，推理仅需7.88毫秒。
- 适合资源受限的自动驾驶竞速平台，降低CPU负载8.3倍。
自主驾驶中的感知几乎依赖神经网络，但自主竞速领域常使用计算能力受限的小型机器人以降低成本并保障安全。因此，对手检测与追踪系统通常采用传统计算机视觉方法。本文提出TinyCenterSpeed，是针对经典CenterPoint方法的轻量化改进，专为1:10比例的自主竞速平台设计，可在仅由中央处理器（CPU）供电的车载计算单元（OBC）上运行，通过外接张量处理单元（TPU）实现高效推理。实验表明，相比当前最先进方法自适应断点检测器（ABD），TinyCenterSpeed在检测与速度估计性能上最高提升61.38%，支持多对手检测与状态估计。其在TPU上的推理时间仅为7.88毫秒，使CPU利用率降低8.3倍，满足实时性要求。
原文摘要 · Abstract (English)
Perception within autonomous driving is nearly synonymous with Neural Networks (NNs). Yet, the domain of autonomous racing is often characterized by scaled, computationally limited robots used for cost-effectiveness and safety. For this reason, opponent detection and tracking systems typically resort to traditional computer vision techniques due to computational constraints. This paper introduces TinyCenterSpeed, a streamlined adaptation of the seminal CenterPoint method, optimized for real-time performance on 1:10 scale autonomous racing platforms. This adaptation is viable even on OBCs powered solely by Central Processing Units (CPUs), as it incorporates the use of an external Tensor Processing Unit (TPU). We demonstrate that, compared to Adaptive Breakpoint Detector (ABD), the current State-of-the-Art (SotA) in scaled autonomous racing, TinyCenterSpeed not only improves detection and velocity estimation by up to 61.38% but also supports multi-opponent detection and estimation. It achieves real-time performance with an inference time of just 7.88 ms on the TPU, significantly reducing CPU utilization 8.3-fold.
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