Maneuver-based Anchor Trajectory Hypotheses at Roundabouts

• URL: http://arxiv.org/abs/2104.11180v1
• Date: Thu, 22 Apr 2021 17:08:29 GMT
• Title: Maneuver-based Anchor Trajectory Hypotheses at Roundabouts
• Authors: Mohamed Hasan, Evangelos Paschalidis, Albert Solernou, He Wang, Gustav Markkula and Richard Romano
• Abstract summary: We address the problem of vehicle motion prediction in a challenging roundabout environment by learning from human data. Drivers' intentions are encoded by a set of maneuvers that correspond to semantic driving concepts. Our model employs a set of maneuver-specific anchor trajectories that cover the space of possible outcomes at the roundabout.
• Score: 3.5851903214591663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting future behavior of the surrounding vehicles is crucial for self-driving platforms to safely navigate through other traffic. This is critical when making decisions like crossing an unsignalized intersection. We address the problem of vehicle motion prediction in a challenging roundabout environment by learning from human driver data. We extend existing recurrent encoder-decoder models to be advantageously combined with anchor trajectories to predict vehicle behaviors on a roundabout. Drivers' intentions are encoded by a set of maneuvers that correspond to semantic driving concepts. Accordingly, our model employs a set of maneuver-specific anchor trajectories that cover the space of possible outcomes at the roundabout. The proposed model can output a multi-modal distribution over the predicted future trajectories based on the maneuver-specific anchors. We evaluate our model using the public RounD dataset and the experiment results show the effectiveness of the proposed maneuver-based anchor regression in improving prediction accuracy, reducing the average RMSE to 28% less than the best baseline. Our code is available at https://github.com/m-hasan-n/roundabout.

Related papers

• Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space [3.487370856323828]
  This work introduces an integrated prediction and planning approach that incorporates the uncertainties of predicted pedestrian states in the training of a model-free DRL algorithm. A novel reward function encourages the AV to respect pedestrians' personal space, decrease speed during close approaches, and minimize the collision probability with their predicted paths. Results show a 40% decrease in collision rate and a 15% increase in minimum distance to pedestrians compared to the state of the art model that does not account for prediction uncertainty.
  arXiv  Detail & Related papers  (2024-05-22T20:09:21Z)
• Probabilistic Prediction of Longitudinal Trajectory Considering Driving Heterogeneity with Interpretability [12.929047288003213]
  This study proposes a trajectory prediction framework that combines Mixture Density Networks (MDN) and considers the driving heterogeneity to provide probabilistic and personalized predictions. The proposed framework is tested based on a wide-range vehicle trajectory dataset.
  arXiv  Detail & Related papers  (2023-12-19T12:56:56Z)
• 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)
• Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information [50.40632021583213]
  Traditional approaches to prediction of future trajectory of road agents rely on knowing information about their past trajectory. This work instead relies on having knowledge of the current state and intended direction to make predictions for multiple vehicles at intersections. Message passing of this information between the vehicles provides each one of them a more holistic overview of the environment.
  arXiv  Detail & Related papers  (2023-01-06T15:13:23Z)
• Multi-modal Transformer Path Prediction for Autonomous Vehicle [15.729029675380083]
  We propose a path prediction system named Multi-modal Transformer Path Prediction (MTPP) that aims to predict long-term future trajectory of target agents. To achieve more accurate path prediction, the Transformer architecture is adopted in our model. An extensive evaluation is conducted to show the efficacy of the proposed system using nuScene, a real-world trajectory forecasting dataset.
  arXiv  Detail & Related papers  (2022-08-15T15:09:26Z)
• Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion [87.77727495366702]
  We introduce the new task of pedestrian stop and go forecasting. Considering the lack of suitable existing datasets for it, we release TRANS, a benchmark for explicitly studying the stop and go behaviors of pedestrians in urban traffic. We build it from several existing datasets annotated with pedestrians' walking motions, in order to have various scenarios and behaviors.
  arXiv  Detail & Related papers  (2022-03-04T18:39:31Z)
• Maneuver-Aware Pooling for Vehicle Trajectory Prediction [3.5851903214591663]
  This paper focuses on predicting the behavior of the surrounding vehicles of an autonomous vehicle on highways. We propose a novel pooling strategy to capture the inter-dependencies between the neighbor vehicles. We incorporated the proposed pooling mechanism into a generative encoder-decoder model, and evaluated our method on the public NGSIM dataset.
  arXiv  Detail & Related papers  (2021-04-29T02:12:08Z)
• IntentNet: Learning to Predict Intention from Raw Sensor Data [86.74403297781039]
  In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
  arXiv  Detail & Related papers  (2021-01-20T00:31:52Z)
• 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)
• 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)
• Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
  Reasoning over visual data is a desirable capability for robotics and vision-based applications. In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent. Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
  arXiv  Detail & Related papers  (2020-02-20T18:50:44Z)

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.