论文标题
部分可观测时空混沌系统的无模型预测
Real-Time And Robust 3D Object Detection with Roadside LiDARs
论文作者
论文摘要
这项工作旨在通过使用路边激光射击环境的3D感知来应对自动驾驶的挑战。我们设计了一个3D对象检测模型，该模型可以实时检测路边激光雷达的交通参与者。我们的模型使用现有的3D检测器作为基线并提高其准确性。为了证明我们提出的模块的有效性，我们在三个不同的车辆和基础设施数据集上训练和评估模型。为了显示我们探测器的域适应能力，我们在来自中国的基础设施数据集上训练它，并在德国记录的其他数据集上进行转移学习。我们为检测器中每个模块进行了几套实验和消融研究，这表明我们的模型的表现优于基线，而推理速度为45 Hz（22 ms）。我们对基于激光雷达的3D探测器做出了重大贡献，可用于智能城市应用程序，以提供连接和自动化的车辆具有深远的视野。连接到路边传感器的车辆可以获取有关拐角处其他车辆的信息，以改善其道路和操纵计划并提高道路交通安全性。
This work aims to address the challenges in autonomous driving by focusing on the 3D perception of the environment using roadside LiDARs. We design a 3D object detection model that can detect traffic participants in roadside LiDARs in real-time. Our model uses an existing 3D detector as a baseline and improves its accuracy. To prove the effectiveness of our proposed modules, we train and evaluate the model on three different vehicle and infrastructure datasets. To show the domain adaptation ability of our detector, we train it on an infrastructure dataset from China and perform transfer learning on a different dataset recorded in Germany. We do several sets of experiments and ablation studies for each module in the detector that show that our model outperforms the baseline by a significant margin, while the inference speed is at 45 Hz (22 ms). We make a significant contribution with our LiDAR-based 3D detector that can be used for smart city applications to provide connected and automated vehicles with a far-reaching view. Vehicles that are connected to the roadside sensors can get information about other vehicles around the corner to improve their path and maneuver planning and to increase road traffic safety.
