Destination Prediction Based on Partial Trajectory Data

• URL: http://arxiv.org/abs/2004.07473v1
• Date: Thu, 16 Apr 2020 06:26:10 GMT
• Title: Destination Prediction Based on Partial Trajectory Data
• Authors: Patrick Ebel, Ibrahim Emre G\"ol, Christoph Lingenfelder and Andreas Vogelsang
• Abstract summary: Two-thirds of people who buy a new car prefer to use a substitute instead of the built-in navigation system. For many applications, knowledge about a user's intended destination and route is crucial. Our approach predicts probable destinations and routes of a vehicle, based on the most recent partial trajectory and additional contextual data.
• Score: 4.783019576803369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Two-thirds of the people who buy a new car prefer to use a substitute instead of the built-in navigation system. However, for many applications, knowledge about a user's intended destination and route is crucial. For example, suggestions for available parking spots close to the destination can be made or ride-sharing opportunities along the route are facilitated. Our approach predicts probable destinations and routes of a vehicle, based on the most recent partial trajectory and additional contextual data. The approach follows a three-step procedure: First, a $k$-d tree-based space discretization is performed, mapping GPS locations to discrete regions. Secondly, a recurrent neural network is trained to predict the destination based on partial sequences of trajectories. The neural network produces destination scores, signifying the probability of each region being the destination. Finally, the routes to the most probable destinations are calculated. To evaluate the method, we compare multiple neural architectures and present the experimental results of the destination prediction. The experiments are based on two public datasets of non-personalized, timestamped GPS locations of taxi trips. The best performing models were able to predict the destination of a vehicle with a mean error of 1.3 km and 1.43 km respectively.

Related papers

• Learning 3D Perception from Others' Predictions [64.09115694891679]
  We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector. For example, when a self-driving car enters a new area, it may learn from other traffic participants whose detectors have been optimized for that area.
  arXiv  Detail & Related papers  (2024-10-03T16:31:28Z)
• Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network [15.950227451262919]
  The application of autonomous driving in open-pit mining has garnered increasing attention for achieving safe and efficient transportation. A method is proposed to predict multiple possible trajectories and their probabilities of the target vehicle. The method underwent offline testing on a dataset specifically designed for autonomous driving scenarios in open-pit mining.
  arXiv  Detail & Related papers  (2024-09-27T02:29:02Z)
• GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction [1.9253333342733674]
  We propose a new trajectory prediction neural network based on the goal areas of a pedestrian. GoalNet significantly improves the previous state-of-the-art performance by 48.7% on the JAAD and 40.8% on the PIE dataset.
  arXiv  Detail & Related papers  (2024-02-29T09:53:19Z)
• Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Network [5.092311422459955]
  In last-mile delivery, drivers deviate from planned routes because of their tacit knowledge of the road and curbside infrastructure. Being able to predict the actual stop sequence that a human driver would follow can help to improve route planning in last-mile delivery. This paper proposes a pair-wise attention-based pointer neural network for this prediction task using drivers' historical delivery trajectory data.
  arXiv  Detail & Related papers  (2023-01-10T06:11:20Z)
• 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)
• ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints [94.60414567852536]
  Long-range navigation requires both planning and reasoning about local traversability. We propose a learning-based approach that integrates learning and planning. ViKiNG can leverage its image-based learned controller and goal-directed to navigate to goals up to 3 kilometers away.
  arXiv  Detail & Related papers  (2022-02-23T02:14:23Z)
• PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D [10.580548257913843]
  We propose a new pedestrian action prediction dataset created by adding per-frame 2D/3D bounding box and behavioral annotations to nuScenes. In addition, we propose a hybrid neural network architecture that incorporates various data modalities for predicting pedestrian crossing action.
  arXiv  Detail & Related papers  (2020-12-14T18:13:44Z)
• Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles [10.485790589381704]
  Trajectory prediction is crucial for autonomous vehicles. We propose a novel instance-aware representation for lane representation. We show that the proposed lane representation together with the lane attention module can be integrated into the widely used encoder-decoder framework.
  arXiv  Detail & Related papers  (2020-07-06T07:57:23Z)
• 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)
• It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction [59.027152973975575]
  We present Predicted Conditioned Network (PECNet) for flexible human trajectory prediction. PECNet infers distant endpoints to assist in long-range multi-modal trajectory prediction. We show that PECNet improves state-of-the-art performance on the Stanford Drone trajectory prediction benchmark by 20.9% and on the ETH/UCY benchmark by 40.8%.
  arXiv  Detail & Related papers  (2020-04-04T21:27:13Z)
• 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.