Improving Diversity of Multiple Trajectory Prediction based on Map-adaptive Lane Loss

• URL: http://arxiv.org/abs/2206.08641v1
• Date: Fri, 17 Jun 2022 09:09:51 GMT
• Title: Improving Diversity of Multiple Trajectory Prediction based on Map-adaptive Lane Loss
• Authors: Sanmin Kim, Hyeongseok Jeon, Junwon Choi, and Dongsuk Kum
• Abstract summary: This study proposes a novel loss function, textitLane Loss, that ensures map-adaptive diversity and accommodates geometric constraints. Experiments performed on the Argoverse dataset show that the proposed method significantly improves the diversity of the predicted trajectories.
• Score: 12.963269946571476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prior arts in the field of motion predictions for autonomous driving tend to focus on finding a trajectory that is close to the ground truth trajectory. Such problem formulations and approaches, however, frequently lead to loss of diversity and biased trajectory predictions. Therefore, they are unsuitable for real-world autonomous driving where diverse and road-dependent multimodal trajectory predictions are critical for safety. To this end, this study proposes a novel loss function, \textit{Lane Loss}, that ensures map-adaptive diversity and accommodates geometric constraints. A two-stage trajectory prediction architecture with a novel trajectory candidate proposal module, \textit{Trajectory Prediction Attention (TPA)}, is trained with Lane Loss encourages multiple trajectories to be diversely distributed, covering feasible maneuvers in a map-aware manner. Furthermore, considering that the existing trajectory performance metrics are focusing on evaluating the accuracy based on the ground truth future trajectory, a quantitative evaluation metric is also suggested to evaluate the diversity of predicted multiple trajectories. The experiments performed on the Argoverse dataset show that the proposed method significantly improves the diversity of the predicted trajectories without sacrificing the prediction accuracy.

Related papers

• TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction [16.188078087197106]
  We propose TrajDiffuse, a planning-based trajectory prediction method using a novel guided conditional diffusion model. We form the trajectory prediction problem as a denoising impaint task and design a map-based guidance term for the diffusion process. TrajDiffuse is able to generate trajectory predictions that match or exceed the accuracy and diversity of the SOTA, while adhering almost perfectly to environmental constraints.
  arXiv  Detail & Related papers  (2024-10-14T17:59:03Z)
• 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)
• Motion Forecasting via Model-Based Risk Minimization [8.766024024417316]
  We propose a novel sampling method applicable to trajectory prediction based on the predictions of multiple models. We first show that conventional sampling based on predicted probabilities can degrade performance due to missing alignment between models. By using state-of-the-art models as base learners, our approach constructs diverse and effective ensembles for optimal trajectory sampling.
  arXiv  Detail & Related papers  (2024-09-16T09:03:28Z)
• 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)
• Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision [2.365702128814616]
  Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving. We propose a middle-ground where multiple trajectory segments are chained together. Our proposed Multi-Branch Self-Supervised Predictor receives additional training on new predictions starting at intermediate future segments.
  arXiv  Detail & Related papers  (2023-06-06T02:46:28Z)
• Adaptive Trajectory Prediction via Transferable GNN [74.09424229172781]
  We propose a novel Transferable Graph Neural Network (T-GNN) framework, which jointly conducts trajectory prediction as well as domain alignment in a unified framework. Specifically, a domain invariant GNN is proposed to explore the structural motion knowledge where the domain specific knowledge is reduced. An attention-based adaptive knowledge learning module is further proposed to explore fine-grained individual-level feature representation for knowledge transfer.
  arXiv  Detail & Related papers  (2022-03-09T21:08:47Z)
• Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding [121.66374635092097]
  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.
  arXiv  Detail & Related papers  (2022-02-03T09:09:56Z)
• Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction [71.97877759413272]
  Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. Our work addresses two key challenges in trajectory prediction, learning outputs, and better predictions by imposing constraints using driving knowledge.
  arXiv  Detail & Related papers  (2021-04-16T17:58:56Z)
• Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss [1.1470070927586014]
  We propose a loss function which enhances trajectory prediction models by enforcing expected driving rules on all predicted modes. Our contribution to trajectory prediction is twofold; we propose a new metric which addresses failure cases of the off-road rate metric. We then use this auxiliary loss to extend the the standard multiple trajectory prediction (MTP) and MultiPath models.
  arXiv  Detail & Related papers  (2020-11-12T22:51:25Z)
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
  We propose advances that address two key challenges in future trajectory prediction. multimodality in both training data and predictions and constant time inference regardless of number of agents.
  arXiv  Detail & Related papers  (2020-07-26T08:17:10Z)
• TPNet: Trajectory Proposal Network for Motion Prediction [81.28716372763128]
  Trajectory Proposal Network (TPNet) is a novel two-stage motion prediction framework. TPNet first generates a candidate set of future trajectories as hypothesis proposals, then makes the final predictions by classifying and refining the proposals. Experiments on four large-scale trajectory prediction datasets, show that TPNet achieves the state-of-the-art results both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-04-26T00:01:49Z)

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.