PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
This addresses the critical problem of real-time environment understanding for autonomous vehicles, representing an incremental advance by integrating tracking into a trainable framework.
The paper tackles joint perception and motion forecasting for self-driving vehicles by proposing PnPNet, an end-to-end model that outputs object tracks and future trajectories from sequential sensor data, showing significant improvements in occlusion recovery and prediction accuracy over state-of-the-art methods on large-scale datasets.
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.