Injecting Knowledge in Data-driven Vehicle Trajectory Predictors

• URL: http://arxiv.org/abs/2103.04854v1
• Date: Mon, 8 Mar 2021 16:03:09 GMT
• Title: Injecting Knowledge in Data-driven Vehicle Trajectory Predictors
• Authors: Mohammadhossein Bahari, Ismail Nejjar, Alexandre Alahi
• Abstract summary: Vehicle trajectory prediction tasks have been commonly tackled from two perspectives: knowledge-driven or data-driven. In this paper, we propose to learn a "Realistic Residual Block" (RRB) which effectively connects these two perspectives. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty.
• Score: 82.91398970736391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle trajectory prediction tasks have been commonly tackled from two distinct perspectives: either with knowledge-driven methods or more recently with data-driven ones. On the one hand, we can explicitly implement domain-knowledge or physical priors such as anticipating that vehicles will follow the middle of the roads. While this perspective leads to feasible outputs, it has limited performance due to the difficulty to hand-craft complex interactions in urban environments. On the other hand, recent works use data-driven approaches which can learn complex interactions from the data leading to superior performance. However, generalization, \textit{i.e.}, having accurate predictions on unseen data, is an issue leading to unrealistic outputs. In this paper, we propose to learn a "Realistic Residual Block" (RRB), which effectively connects these two perspectives. Our RRB takes any off-the-shelf knowledge-driven model and finds the required residuals to add to the knowledge-aware trajectory. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty. We also constrain our output with Model Predictive Control (MPC) to satisfy kinematic constraints. Using a publicly available dataset, we show that our method outperforms previous works in terms of accuracy and generalization to new scenes. We will release our code and data split here: https://github.com/vita-epfl/RRB.

Related papers

• T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory [39.021321011792786]
  Trajectory prediction is a challenging problem that requires considering interactions among multiple actors. Data-driven approaches have been used to address this complex problem, but they suffer from unreliable predictions under distribution shifts during test time. We propose several online learning methods using regression loss from the ground truth of observed data. Our method surpasses the performance of existing state-of-the-art online learning methods in terms of both prediction accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-03-15T06:47:14Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• 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)
• Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes [38.43491956142818]
  Planning task involves predicting the trajectory of the ego vehicle based on inputs from both internal intention and the external environment. Most existing works evaluate their performance on the nuScenes dataset using the L2 error and collision rate between the predicted trajectories and the ground truth. In this paper, we reevaluate these existing evaluation metrics and explore whether they accurately measure the superiority of different methods. Our simple method achieves similar end-to-end planning performance on the nuScenes dataset with other perception-based methods, reducing the average L2 error by about 20%.
  arXiv  Detail & Related papers  (2023-05-17T17:59:11Z)
• PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map [58.53373202647576]
  We propose PreTraM, a self-supervised pre-training scheme for trajectory forecasting. It consists of two parts: 1) Trajectory-Map Contrastive Learning, where we project trajectories and maps to a shared embedding space with cross-modal contrastive learning, and 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively in FDE-10 on the challenging nuScenes dataset.
  arXiv  Detail & Related papers  (2022-04-21T23:01:21Z)
• PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer [0.9786690381850356]
  We introduce a model called PRediction Transformer (PReTR) that extracts features from the multi-agent scenes by employing a factorized-temporal attention module. It shows less computational needs than previously studied models with empirically better results. We leverage encoder-decoder Transformer networks for parallel decoding a set of learned object queries.
  arXiv  Detail & Related papers  (2022-03-17T12:52:23Z)
• Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving [13.861631911491651]
  Self-attention enables better control over representing the agent's social context. We show improvements on standard metrics over various baselines on the Argoverse dataset.
  arXiv  Detail & Related papers  (2020-11-30T15:42:15Z)
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift [81.74795324629712]
  We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift. We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness. The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
  arXiv  Detail & Related papers  (2020-06-19T05:08:43Z)
• Value-driven Hindsight Modelling [68.658900923595]
  Value estimation is a critical component of the reinforcement learning (RL) paradigm. Model learning can make use of the rich transition structure present in sequences of observations, but this approach is usually not sensitive to the reward function. We develop an approach for representation learning in RL that sits in between these two extremes. This provides tractable prediction targets that are directly relevant for a task, and can thus accelerate learning the value function.
  arXiv  Detail & Related papers  (2020-02-19T18:10:20Z)

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.