Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities

• URL: http://arxiv.org/abs/2401.05322v1
• Date: Wed, 10 Jan 2024 18:41:39 GMT
• Title: Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities
• Authors: Carolin Schmidt, Mathias Tygesen, Filipe Rodrigues
• Abstract summary: This study presents an AT prediction system for autonomous shuttles, utilizing separate models for dwell and running time predictions. To accurately handle the case of a shuttle bypassing a stop, we propose a hierarchical model combining a random forest classifier and a GNN. The results for the final AT prediction are promising, showing low errors even when predicting several stops ahead.
• Score: 3.6294895527930504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban mobility is on the cusp of transformation with the emergence of shared, connected, and cooperative automated vehicles. Yet, for them to be accepted by customers, trust in their punctuality is vital. Many pilot initiatives operate without a fixed schedule, thus enhancing the importance of reliable arrival time (AT) predictions. This study presents an AT prediction system for autonomous shuttles, utilizing separate models for dwell and running time predictions, validated on real-world data from five cities. Alongside established methods such as XGBoost, we explore the benefits of integrating spatial data using graph neural networks (GNN). To accurately handle the case of a shuttle bypassing a stop, we propose a hierarchical model combining a random forest classifier and a GNN. The results for the final AT prediction are promising, showing low errors even when predicting several stops ahead. Yet, no single model emerges as universally superior, and we provide insights into the characteristics of pilot sites that influence the model selection process. Finally, we identify dwell time prediction as the key determinant in overall AT prediction accuracy when autonomous shuttles are deployed in low-traffic areas or under regulatory speed limits. This research provides insights into the current state of autonomous public transport prediction models and paves the way for more data-informed decision-making as the field advances.

Related papers

• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  Motion planners (MPs) are crucial for safe navigation in complex urban environments. nuPlan, a recently released MP benchmark, addresses this limitation by augmenting real-world driving logs with closed-loop simulation logic. We present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• Valeo4Cast: A Modular Approach to End-to-End Forecasting [93.86257326005726]
  Our solution ranks first in the Argoverse 2 End-to-end Forecasting Challenge, with 63.82 mAPf. We depart from the current trend of tackling this task via end-to-end training from perception to forecasting, and instead use a modular approach. We surpass forecasting results by +17.1 points over last year's winner and by +13.3 points over this year's runner-up.
  arXiv  Detail & Related papers  (2024-06-12T11:50:51Z)
• 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)
• 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)
• Adaptive Modeling of Uncertainties for Traffic Forecasting [33.81668910587541]
  QuanTraffic is a generic framework to enhance the capability of an arbitrary DNN model for uncertainty modeling. It automatically learns a standard quantile function during the DNN model training to produce a prediction interval for the single-point prediction. It dynamically adjusts the prediction interval based on the location and prediction window of the test input.
  arXiv  Detail & Related papers  (2023-03-16T12:56:13Z)
• 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)
• Exploring Attention GAN for Vehicle Motion Prediction [2.887073662645855]
  We study the influence of attention in generative models for motion prediction, considering both physical and social context. We validate our method using the Argoverse Motion Forecasting Benchmark 1.1, achieving competitive unimodal results.
  arXiv  Detail & Related papers  (2022-09-26T13:18:32Z)
• Causal-based Time Series Domain Generalization for Vehicle Intention Prediction [19.944268567657307]
  Accurately predicting possible behaviors of traffic participants is an essential capability for autonomous vehicles. In this paper, we aim to address the domain generalization problem for vehicle intention prediction tasks. Our proposed method has consistent improvement on prediction accuracy compared to other state-of-the-art domain generalization and behavior prediction methods.
  arXiv  Detail & Related papers  (2021-12-03T18:58:07Z)
• Self-Supervised Action-Space Prediction for Automated Driving [0.0]
  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.
  arXiv  Detail & Related papers  (2021-09-21T08:27:56Z)
• Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases [10.41902340952981]
  We propose a novel Graph Convolutional Neural Network (GCNN)-based approach, Attentional-GCNN, which aggregates information of implicit interaction between pedestrians in a crowd by assigning attention weight in edges of the graph. We show our proposed method achieves an improvement over the state of art by 10% Average Displacement Error (ADE) and 12% Final Displacement Error (FDE) with fast inference speeds.
  arXiv  Detail & Related papers  (2020-11-23T03:13:26Z)
• 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)

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.