论文标题
PNPNET：端到端的感知和预测循环中的跟踪
PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
论文作者
论文摘要
我们解决了在自动驾驶汽车的背景下解决联合感知和运动预测的问题。为了实现这一目标，我们提出了PNPNET，这是一种端到端模型，它作为输入顺序传感器数据，并在每个时间步骤对象轨迹及其未来轨迹上输出。关键组件是一个新颖的跟踪模块，该模块可从检测和利用轨迹级别的特征在线生成对象轨迹，以进行运动预测。具体而言，对象轨迹通过解决数据关联问题和轨迹估计问题来在每个时间步骤进行更新。重要的是，整个模型都是端到端的训练，并从所有任务的联合优化中受益。我们在两个大规模驾驶数据集上验证了PNPNET，并以更好的遮挡恢复和更准确的未来预测显示出对最先进的显着改善。
We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. Towards this goal we propose PnPNet, an end-to-end model that takes as input sequential sensor data, and outputs at each time step object tracks and their future trajectories. The key component is a novel tracking module that generates object tracks online from detections and exploits trajectory level features for motion forecasting. Specifically, the object tracks get updated at each time step by solving both the data association problem and the trajectory estimation problem. Importantly, the whole model is end-to-end trainable and benefits from joint optimization of all tasks. We validate PnPNet on two large-scale driving datasets, and show significant improvements over the state-of-the-art with better occlusion recovery and more accurate future prediction.
