用行车记录仪视频精准预测车辆运动状态，为自动驾驶感知提供新思路。
Estimation of Kinematic Motion from Dashcam Footage
- 基于车载CAN总线与同步摄像头数据，构建神经网络模型
- 实现对车速、偏航角及前车相对位置速度的高精度预测
- 开源工具+市售设备即可复现，适合自动驾驶研究者参考
本文旨在探索行车记录仪视频在预测类车车辆实际运动状态方面的准确性。研究采用车辆车载数据流（通过控制器局域网）与时间同步的仪表盘摄像头，采集了18小时的行车数据。提出神经网络模型，可量化预测车辆速度、偏航角，以及前方车辆的存在性、相对距离和相对速度。此外，论文还介绍了如何使用开源工具和现成设备收集类似数据，供其他研究者复现实验。
原文摘要 · Abstract (English)
The goal of this paper is to explore the accuracy of dashcam footage to predict the actual kinematic motion of a car-like vehicle. Our approach uses ground truth information from the vehicle's on-board data stream, through the controller area network, and a time-synchronized dashboard camera, mounted to a consumer-grade vehicle, for 18 hours of footage and driving. The contributions of the paper include neural network models that allow us to quantify the accuracy of predicting the vehicle speed and yaw, as well as the presence of a lead vehicle, and its relative distance and speed. In addition, the paper describes how other researchers can gather their own data to perform similar experiments, using open-source tools and off-the-shelf technology.
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