The WayHome: Long-term Motion Prediction on Dynamically Scaled

• URL: http://arxiv.org/abs/2310.04232v1
• Date: Fri, 6 Oct 2023 13:17:46 GMT
• Title: The WayHome: Long-term Motion Prediction on Dynamically Scaled
• Authors: Kay Scheerer, Thomas Michalke, Juergen Mathes
• Abstract summary: One of the key challenges for autonomous vehicles is the ability to accurately predict the motion of other objects in the surrounding environment. We predict multiple heatmaps with a neuralnetwork-based model for every traffic participant in the vicinity of the autonomous vehicle. Heatmaps are used as input to a novel sampling algorithm that extracts coordinates corresponding to the most likely future positions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the key challenges for autonomous vehicles is the ability to accurately predict the motion of other objects in the surrounding environment, such as pedestrians or other vehicles. In this contribution, a novel motion forecasting approach for autonomous vehicles is developed, inspired by the work of Gilles et al. [1]. We predict multiple heatmaps with a neuralnetwork-based model for every traffic participant in the vicinity of the autonomous vehicle; with one heatmap per timestep. The heatmaps are used as input to a novel sampling algorithm that extracts coordinates corresponding to the most likely future positions. We experiment with different encoders and decoders, as well as a comparison of two loss functions. Additionally, a new grid-scaling technique is introduced, showing further improved performance. Overall, our approach improves stateof-the-art miss rate performance for the function-relevant prediction interval of 3 seconds while being competitive in longer prediction intervals (up to eight seconds). The evaluation is done on the public 2022 Waymo motion challenge.

Related papers

• Motion Forecasting in Continuous Driving [41.6423398623095]
  In autonomous driving, motion forecasting takes place repeatedly and continuously as the self-driving car moves. Existing forecasting methods process each driving scene within a certain range independently. We propose a novel motion forecasting framework for continuous driving, named RealMotion.
  arXiv  Detail & Related papers  (2024-10-08T13:04:57Z)
• OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment [0.0]
  We introduce an end-to-end neural network methodology designed to predict the future behaviors of all dynamic objects in the environment. We propose a novel time-weighted motion flow loss, whose application has shown a substantial decrease in end-point error.
  arXiv  Detail & Related papers  (2024-04-02T19:37:58Z)
• AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving [59.94343412438211]
  We introduce the GPT style next token motion prediction into motion prediction. Different from language data which is composed of homogeneous units -words, the elements in the driving scene could have complex spatial-temporal and semantic relations. We propose to adopt three factorized attention modules with different neighbors for information aggregation and different position encoding styles to capture their relations.
  arXiv  Detail & Related papers  (2024-03-20T06:22:37Z)
• Learning Pedestrian Actions to Ensure Safe Autonomous Driving [12.440017892152417]
  It is critical for Autonomous Vehicles to have the ability to predict pedestrians' short-term and immediate actions in real-time. In this work, a novel multi-task sequence to sequence Transformer encoders-decoders (TF-ed) architecture is proposed for pedestrian action and trajectory prediction. The proposed approach is compared against an existing LSTM encoders decoders (LSTM-ed) architecture for action and trajectory prediction.
  arXiv  Detail & Related papers  (2023-05-22T14:03:38Z)
• 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)
• MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving [6.10183951877597]
  We present a simple and yet very strong baseline for multimodal motion prediction based purely on Convolutional Neural Networks. Our source code is publicly available at GitHub.
  arXiv  Detail & Related papers  (2022-06-05T12:31:58Z)
• 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)
• Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset [84.3946567650148]
  With over 100,000 scenes, each 20 seconds long at 10 Hz, our new dataset contains more than 570 hours of unique data over 1750 km of roadways. We use a high-accuracy 3D auto-labeling system to generate high quality 3D bounding boxes for each road agent. We introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
  arXiv  Detail & Related papers  (2021-04-20T17:19:05Z)
• 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)
• Motion Prediction Using Temporal Inception Module [96.76721173517895]
  We propose a Temporal Inception Module (TIM) to encode human motion. Our framework produces input embeddings using convolutional layers, by using different kernel sizes for different input lengths. The experimental results on standard motion prediction benchmark datasets Human3.6M and CMU motion capture dataset show that our approach consistently outperforms the state of the art methods.
  arXiv  Detail & Related papers  (2020-10-06T20:26:01Z)
• 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.