Self-Supervised Action-Space Prediction for Automated Driving

• URL: http://arxiv.org/abs/2109.10024v1
• Date: Tue, 21 Sep 2021 08:27:56 GMT
• Title: Self-Supervised Action-Space Prediction for Automated Driving
• Authors: Faris Janjo\v{s}, Maxim Dolgov, J. Marius Z\"ollner
• Abstract summary: We present a novel learned multi-modal trajectory prediction architecture for automated driving. It achieves kinematically feasible predictions by casting the learning problem into the space of accelerations and steering angles. The proposed methods are evaluated on real-world datasets containing urban intersections and roundabouts.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Making informed driving decisions requires reliable prediction of other vehicles' trajectories. In this paper, we present a novel learned multi-modal trajectory prediction architecture for automated driving. It achieves kinematically feasible predictions by casting the learning problem into the space of accelerations and steering angles -- by performing action-space prediction, we can leverage valuable model knowledge. Additionally, the dimensionality of the action manifold is lower than that of the state manifold, whose intrinsically correlated states are more difficult to capture in a learned manner. For the purpose of action-space prediction, we present the simple Feed-Forward Action-Space Prediction (FFW-ASP) architecture. Then, we build on this notion and introduce the novel Self-Supervised Action-Space Prediction (SSP-ASP) architecture that outputs future environment context features in addition to trajectories. A key element in the self-supervised architecture is that, based on an observed action history and past context features, future context features are predicted prior to future trajectories. The proposed methods are evaluated on real-world datasets containing urban intersections and roundabouts, and show accurate predictions, outperforming state-of-the-art for kinematically feasible predictions in several prediction metrics.

Related papers

• Traj-Explainer: An Explainable and Robust Multi-modal Trajectory Prediction Approach [12.60529039445456]
  Navigating complex traffic environments has been significantly enhanced by advancements in intelligent technologies, enabling accurate environment perception and trajectory prediction for automated vehicles. Existing research often neglects the consideration of the joint reasoning of scenario agents and lacks interpretability in trajectory prediction models. An explainability-oriented trajectory prediction model is designed in this work, named Explainable Diffusion Conditional based Multimodal Trajectory Prediction Traj-Explainer.
  arXiv  Detail & Related papers  (2024-10-22T08:17:33Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• An End-to-End Vehicle Trajcetory Prediction Framework [3.7311680121118345]
  An accurate prediction of a future trajectory does not just rely on the previous trajectory, but also a simulation of the complex interactions between other vehicles nearby. Most state-of-the-art networks built to tackle the problem assume readily available past trajectory points. We propose a novel end-to-end architecture that takes raw video inputs and outputs future trajectory predictions.
  arXiv  Detail & Related papers  (2023-04-19T15:42:03Z)
• LOPR: Latent Occupancy PRediction using Generative Models [49.15687400958916]
  LiDAR generated occupancy grid maps (L-OGMs) offer a robust bird's eye-view scene representation. We propose a framework that decouples occupancy prediction into: representation learning and prediction within the learned latent space.
  arXiv  Detail & Related papers  (2022-10-03T22:04:00Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• LOKI: Long Term and Key Intentions for Trajectory Prediction [22.097307597204736]
  Recent advances in trajectory prediction have shown that explicit reasoning about agents' intent is important to accurately forecast their motion. We propose LOKI (LOng term and Key Intentions), a novel large-scale dataset that is designed to tackle joint trajectory and intention prediction. We show our method outperforms state-of-the-art trajectory prediction methods by upto $27%$ and also provide a baseline for frame-wise intention estimation.
  arXiv  Detail & Related papers  (2021-08-18T16:57:03Z)
• Learning to Predict Vehicle Trajectories with Model-based Planning [43.27767693429292]
  We introduce a novel framework called PRIME, which stands for Prediction with Model-based Planning. Unlike recent prediction works that utilize neural networks to model scene context, PRIME is designed to generate accurate and feasibility-guaranteed future trajectory predictions. Our PRIME outperforms state-of-the-art methods in prediction accuracy, feasibility, and robustness under imperfect tracking.
  arXiv  Detail & Related papers  (2021-03-06T04:49:24Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)
• 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)
• PiP: Planning-informed Trajectory Prediction for Autonomous Driving [69.41885900996589]
  We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. By informing the prediction process with the planning of ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets.
  arXiv  Detail & Related papers  (2020-03-25T16:09:54Z)
• Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network [29.289670231364788]
  In this paper, we propose a generic generative neural system for multi-agent trajectory prediction. We also employ an efficient kinematic constraint layer applied to vehicle trajectory prediction. The proposed system is evaluated on three public benchmark datasets for trajectory prediction.
  arXiv  Detail & Related papers  (2020-02-14T20:11:13Z)

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.