Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions

• URL: http://arxiv.org/abs/2003.09996v1
• Date: Sun, 22 Mar 2020 21:28:39 GMT
• Title: Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions
• Abstract summary: Current approaches to AV motion planning around crosswalks predict only for short time horizons (1-2 s) We develop a hybrid systems model that uses pedestrians gap acceptance behavior and constant velocity dynamics for long-term pedestrian trajectory prediction. Results demonstrate the applicability of the model for long-term (> 5 s) pedestrian trajectory prediction at crosswalks.
• Score: 11.333580296938859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For safe navigation around pedestrians, automated vehicles (AVs) need to plan their motion by accurately predicting pedestrians trajectories over long time horizons. Current approaches to AV motion planning around crosswalks predict only for short time horizons (1-2 s) and are based on data from pedestrian interactions with human-driven vehicles (HDVs). In this paper, we develop a hybrid systems model that uses pedestrians gap acceptance behavior and constant velocity dynamics for long-term pedestrian trajectory prediction when interacting with AVs. Results demonstrate the applicability of the model for long-term (> 5 s) pedestrian trajectory prediction at crosswalks. Further we compared measures of pedestrian crossing behaviors in the immersive virtual environment (when interacting with AVs) to that in the real world (results of published studies of pedestrians interacting with HDVs), and found similarities between the two. These similarities demonstrate the applicability of the hybrid model of AV interactions developed from an immersive virtual environment (IVE) for real-world scenarios for both AVs and HDVs.

Related papers

• Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning [6.169445656675287]
  This study extracts safety-critical pedestrian-vehicle interactions from the Argoverse 2 dataset.<n>To model vehicle-type-specific pedestrian crash avoidance behavior, we develop a Smooth-Mamba Deep Deterministic Policy Gradient framework.<n>Reconstructed trajectories demonstrate strong behavioral realism, accurately reproducing crash avoidance kinematics in both AV and HDV scenarios.
  arXiv  Detail & Related papers  (2026-05-27T14:44:10Z)
• Where Do You Go? Pedestrian Trajectory Prediction using Scene Features [1.8874331450711404]
  We present a novel trajectory prediction model that integrates both pedestrian interactions and environmental context. Our approach captures spatial and temporal interactions among pedestrians within a sparse graph framework. Our method significantly outperforms existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-01-23T17:15:26Z)
• GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction [12.613528624623514]
  This research is the first to conduct both quantitative and qualitative evaluations of Vision Language Models (VLMs) in the context of pedestrian behavior prediction for autonomous driving. We evaluate GPT-4V on publicly available pedestrian datasets: JAAD and WiDEVIEW. The model achieves a 57% accuracy in a zero-shot manner, which, while impressive, is still behind the state-of-the-art domain-specific models (70%) in predicting pedestrian crossing actions.
  arXiv  Detail & Related papers  (2023-11-24T18:02:49Z)
• Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review [3.809702129519642]
  Planning an autonomous vehicle's path in a space shared with pedestrians requires reasoning about pedestrians' future trajectories. This paper systematically reviews different methods proposed in the literature for modelling pedestrian trajectory prediction.
  arXiv  Detail & Related papers  (2023-08-11T23:58:51Z)
• Local and Global Contextual Features Fusion for Pedestrian Intention Prediction [2.203209457340481]
  We analyse and analyse visual features of both pedestrian and traffic contexts. To understand the global context, we utilise location, motion, and environmental information. These multi-modality features are intelligently fused for effective intention learning.
  arXiv  Detail & Related papers  (2023-05-01T22:37:31Z)
• 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)
• Pedestrian Trajectory Prediction via Spatial Interaction Transformer Network [7.150832716115448]
  In traffic scenes, when encountering with oncoming people, pedestrians may make sudden turns or stop immediately. To predict such unpredictable trajectories, we can gain insights into the interaction between pedestrians. We present a novel generative method named Spatial Interaction Transformer (SIT), which learns the correlation of pedestrian trajectories through attention mechanisms.
  arXiv  Detail & Related papers  (2021-12-13T13:08:04Z)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting [91.69900691029908]
  We advocate for predicting both the individual motions as well as the scene occupancy map. We propose a Scene-Actor Graph Neural Network (SA-GNN) which preserves the relative spatial information of pedestrians. On two large-scale real-world datasets, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods.
  arXiv  Detail & Related papers  (2021-01-07T06:08:21Z)
• What-If Motion Prediction for Autonomous Driving [58.338520347197765]
  Viable solutions must account for both the static geometric context, such as road lanes, and dynamic social interactions arising from multiple actors. We propose a recurrent graph-based attentional approach with interpretable geometric (actor-lane) and social (actor-actor) relationships. Our model can produce diverse predictions conditioned on hypothetical or "what-if" road lanes and multi-actor interactions.
  arXiv  Detail & Related papers  (2020-08-24T17:49:30Z)
• Efficient Behavior-aware Control of Automated Vehicles at Crosswalks using Minimal Information Pedestrian Prediction Model [5.6640402503031515]
  For automated vehicles (AVs) to reliably navigate through crosswalks, they need to understand pedestrians crossing behaviors. We present a Behavior aware Model Predictive Controller (B-MPC) for AVs that incorporates long-term predictions of pedestrian crossing behavior. Results demonstrate the applicability of the controller for safe and efficient navigation at crossing scenarios.
  arXiv  Detail & Related papers  (2020-03-22T21:34:38Z)
• 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.