Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
153 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop (2005.14711v2)

Published 29 May 2020 in cs.CV and cs.RO

Abstract: 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.

Citations (160)

Summary

  • The paper introduces PnPNet, which integrates tracking into the perception-prediction loop to enhance motion forecasting for self-driving vehicles.
  • It employs an LSTM-based trajectory representation to capture temporal dynamics, addressing occlusion challenges and improving detection precision.
  • Experimental results on nuScenes and ATG4D demonstrate notable gains in Average Precision and lower prediction errors versus traditional modular pipelines.

An Analysis of PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

The paper presents PnPNet, a novel approach to tackling the complex task of joint perception and motion forecasting within the context of autonomous driving. This task is essential for predicting the future movements of surrounding objects, a key component for planning safe and efficient maneuvers for self-driving vehicles. The paper addresses the limitations of traditional autonomy systems, which decompose these tasks into discrete subtasks of object detection, object tracking, and motion forecasting, each handled by independent modules. These traditional methods suffer from a lack of integrated processing across tasks, resulting in inefficiencies and reduced overall accuracy due to the compact interfaces between module outputs.

PnPNet innovates by integrating a multi-object tracking framework into the perception and prediction pipeline. The inclusion of tracking allows the model to maintain a rich temporal history, which significantly enhances the ability to perform motion forecasting. Unlike previous models that consider tracking as a separate post-processing step, PnPNet brings tracking into the main loop, enabling more comprehensive utilization of past information. This innovation is facilitated by a novel trajectory representation mechanism that captures temporal features by leveraging LSTM networks to model object dynamics, thus producing richer data for prediction tasks.

The PnPNet framework consists of three main modules: 3D object detection, a discrete-continuous tracking mechanism, and a motion forecasting unit. The model effectively handles the discrete problem of data association, ensuring the correct linking of detections to object tracks over time, and enhances trajectory estimation through continuous tracking. These innovations lead to improvements in perceiving and predicting objects' movements through occlusions and other complex scenarios, outperforming existing models.

Empirical evaluations on the nuScenes and ATG4D datasets demonstrate significant performance improvements over previous state-of-the-art methods. Notably, PnPNet achieves enhanced occlusion recovery and more precise trajectory predictions, as demonstrated by substantial gains in Average Precision (AP) and reduced prediction errors (ADE and FDE) in both datasets. These numerical results underscore the success of integrating tracking into the perception-prediction loop and the efficacy of trajectory-level representations in enhancing both detection and prediction capabilities.

The implications of this research are substantial for the development of autonomous systems, as the ability to accurately forecast the trajectories of surrounding objects is crucial for safe navigation and planning. PnPNet's end-to-end trainable architecture offers a promising avenue for further research and development in autonomous perception systems by addressing the crucial issue of joint optimization across interrelated tasks. Future developments may focus on expanding this paradigm to even more complex scenarios and incorporating higher-level decision-making tasks, such as multi-agent interaction modeling and motion planning, within a unified learning framework.