Online learning for dynamic impending collision prediction using FMCW radar

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2023-08-26 DOI:10.1145/3616018
Aarti Singh, Neal Patwari
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Abstract

Radar collision prediction systems can play a crucial role in safety critical applications, such as autonomous vehicles and smart helmets for contact sports, by predicting impending collision just before it will occur. Collision prediction algorithms use the velocity and range measurements provided by radar to calculate time to collision. However, radar measurements used in such systems contain significant clutter, noise, and inaccuracies which hamper reliability. Existing solutions to reduce clutter are based on static filtering methods. In this paper, we present a deep learning approach using frequency modulated continuous wave (FMCW) radar and inertial sensing that learns the environmental and user-specific conditions that lead to future collisions. We present a process of converting raw radar samples to range-Doppler matrices (RDMs) and then training a deep convolutional neural network that outputs predictions (impending collision vs. none) for any measured RDM. The system is retrained to work in dynamically changing environments and maintain prediction accuracy. We demonstrate the effectiveness of our approach of using the information from radar data to predict impending collisions in real-time via real-world experiments, and show that our method achieves an F1-score of 0.91 and outperforms a traditional approach in accuracy and adaptability.
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基于FMCW雷达的动态碰撞预测在线学习
雷达碰撞预测系统可以在碰撞发生之前预测即将发生的碰撞,在安全关键应用中发挥至关重要的作用,例如自动驾驶汽车和接触式运动的智能头盔。碰撞预测算法使用雷达提供的速度和距离测量来计算碰撞时间。然而,在这种系统中使用的雷达测量包含显著的杂波、噪声和不准确性,从而妨碍了可靠性。现有的减少杂波的解决方案是基于静态过滤方法。在本文中,我们提出了一种使用调频连续波(FMCW)雷达和惯性传感的深度学习方法,该方法可以学习导致未来碰撞的环境和用户特定条件。我们提出了一个将原始雷达样本转换为距离多普勒矩阵(RDM)的过程,然后训练一个深度卷积神经网络,该网络为任何测量的RDM输出预测(即将发生的碰撞与无碰撞)。该系统经过再训练,可以在动态变化的环境中工作,并保持预测的准确性。我们通过现实世界的实验证明了利用雷达数据信息实时预测即将发生碰撞的方法的有效性,并表明我们的方法达到了f1得分0.91,并且在准确性和适应性方面优于传统方法。
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
0
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