Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding

• URL: http://arxiv.org/abs/2202.01478v1
• Date: Thu, 3 Feb 2022 09:09:56 GMT
• Title: Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding
• Authors: Pu Zhang, Lei Bai, Jianru Xue, Jianwu Fang, Nanning Zheng, Wanli Ouyang
• Abstract summary: Trajectories obtained from object detection and tracking are inevitably noisy. We propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories.
• Score: 121.66374635092097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory forecasting is critical for autonomous platforms to make safe planning and actions. Currently, most trajectory forecasting methods assume that object trajectories have been extracted and directly develop trajectory predictors based on the ground truth trajectories. However, this assumption does not hold in practical situations. Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories. In this paper, we propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories. Different from the traditional methods which encode the motion cue of an agent based on its clearly defined trajectory, we extract the motion information only based on the affinity cues among detection results, in which an affinity-aware state update mechanism is designed to take the uncertainty of association into account. In addition, considering that there could be multiple plausible matching candidates, we aggregate the states of them. This design relaxes the undesirable effect of noisy trajectory obtained from data association. Extensive ablation experiments validate the effectiveness of our method and its generalization ability on different detectors. Cross-comparison to other forecasting schemes further proves the superiority of our method. Code will be released upon acceptance.

Related papers

• Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehicles [8.398221841050349]
  Trajectory prediction describes the motions of surrounding moving obstacles for an autonomous vehicle. In this paper, we aim to establish real-time awareness of out-of-distribution in trajectory prediction for autonomous vehicles. Our solutions are lightweight and can handle the occurrence of out-of-distribution at any time during trajectory prediction inference.
  arXiv  Detail & Related papers  (2024-09-25T18:43:58Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Certified Human Trajectory Prediction [66.1736456453465]
  Tray prediction plays an essential role in autonomous vehicles. We propose a certification approach tailored for the task of trajectory prediction. We address the inherent challenges associated with trajectory prediction, including unbounded outputs, and mutli-modality.
  arXiv  Detail & Related papers  (2024-03-20T17:41:35Z)
• An End-to-End Framework of Road User Detection, Tracking, and Prediction from Monocular Images [11.733622044569486]
  We build an end-to-end framework for detection, tracking, and trajectory prediction called ODTP. It adopts the state-of-the-art online multi-object tracking model, QD-3DT, for perception and trains the trajectory predictor, DCENet++, directly based on the detection results. We evaluate the performance of ODTP on the widely used nuScenes dataset for autonomous driving.
  arXiv  Detail & Related papers  (2023-08-09T15:46:25Z)
• Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive? [93.10694819127608]
  We propose a unified evaluation pipeline for forecasting methods with real-world perception inputs. Our in-depth study uncovers a substantial performance gap when transitioning from curated to perception-based data.
  arXiv  Detail & Related papers  (2023-06-15T17:03:14Z)
• Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion [88.45326906116165]
  We present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID) We encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method.
  arXiv  Detail & Related papers  (2022-03-25T16:59:08Z)
• Human Trajectory Prediction via Counterfactual Analysis [87.67252000158601]
  Forecasting human trajectories in complex dynamic environments plays a critical role in autonomous vehicles and intelligent robots. Most existing methods learn to predict future trajectories by behavior clues from history trajectories and interaction clues from environments. We propose a counterfactual analysis method for human trajectory prediction to investigate the causality between the predicted trajectories and input clues.
  arXiv  Detail & Related papers  (2021-07-29T17:41:34Z)
• Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models [12.56249869551208]
  Orientation is an important property for downstream modules of an autonomous system. We present a method that extends the existing models that perform joint object detection and motion prediction. In addition, the approach is able to quantify prediction uncertainty, outputting the probability that the inferred orientation is flipped.
  arXiv  Detail & Related papers  (2020-11-05T21:59:44Z)
• The Unsupervised Method of Vessel Movement Trajectory Prediction [1.2617078020344619]
  This article presents an unsupervised method of ship movement trajectory prediction. It represents the data in a three-dimensional space which consists of time difference between points, the scaled error distance between the tested and its predicted forward and backward locations, and the space-time angle. Unlike most statistical learning or deep learning methods, the proposed clustering-based trajectory reconstruction method does not require computationally expensive model training.
  arXiv  Detail & Related papers  (2020-07-27T17:45:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.