A Method for Pedestrian Trajectory Prediction Using INS-GNSS Wearable Devices
Unlocking Predictive Power for Enterprise Operations
This paper introduces a novel multi-source perception fusion system designed for robust pedestrian trajectory prediction. Utilizing INS-GNSS wearable devices, the system integrates a Gait Adaptive UKF (Gait-AUKF) for precise localization, which dynamically adjusts filter parameters based on gait patterns and motion states. For future trajectory prediction, it employs a multi-source fusion attention mechanism comprising a GRU encoder to extract historical motion features, an attention mechanism to assign varying weights, an LSTM decoder, and an A* path planning algorithm to ensure physically constrained and intent-aligned trajectories. The approach significantly enhances localization and prediction accuracy, effectively addressing spatiotemporal uncertainty and multimodal motion in complex urban environments.
Executive Impact Summary
The core innovation lies in two integrated components: 1. Gait Adaptive UKF (Gait-AUKF): This algorithm significantly improves pedestrian localization by integrating high-precision IMUs and multi-mode GNSS. It dynamically identifies pedestrian gait patterns, adjusts noise statistical characteristics, and applies velocity constraints during the standing phase (ZUPT) to suppress trajectory drift and enhance tracking accuracy, especially during GNSS outages and complex maneuvers. 2. Multi-Source Fusion Attention Mechanism for Prediction: This framework combines a GRU encoder for extracting dynamic motion features, an attention mechanism for assigning scale-varying weights to historical trajectories, an LSTM decoder for generating future paths, and an A* path planning algorithm to impose physical and environmental constraints. This ensures generated trajectories are both behaviorally realistic and physically feasible, overcoming the limitations of traditional models in handling pedestrian randomness and multimodal behavior.
This research delivers substantial enterprise value by: * Enhancing Pedestrian Safety: Providing highly accurate and robust prediction of vulnerable road user (VRU) movements, critical for autonomous driving systems to anticipate and react safely. * Improving Urban Navigation and Monitoring: Enabling more precise tracking and prediction in smart city applications, especially in challenging environments like urban canyons or tunnels where GNSS signals are often degraded. * Optimizing Resource Management: Offering a low-cost, wearable solution for real-time pedestrian positioning, which can be deployed in various scenarios from logistics and security to public safety and crowd management. * Facilitating Advanced AI Systems: The adaptable and robust framework provides a foundational component for next-generation AI systems requiring accurate human movement prediction and planning capabilities.
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Eastward Error Reduction
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Northward Error Reduction
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Vertical Error Reduction
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Prediction APE Reduction
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Prediction DE Reduction
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Ablation ADE Reduction
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Ablation FDE Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Localization Enhancement
The Gait Adaptive UKF (Gait-AUKF) is a novel localization algorithm that fuses IMU and GNSS data while adaptively adjusting noise characteristics based on detected pedestrian gait phases. During the standing phase, it applies zero velocity update (ZUPT) constraints to minimize drift. This adaptive approach ensures high precision localization even during GNSS outages and complex motion patterns, significantly outperforming conventional UKF and AUKF methods by reducing errors in all spatial dimensions.
Prediction Framework
The trajectory prediction framework utilizes a multi-source fusion attention mechanism. A GRU encoder extracts key features from historical pedestrian motion, which are then processed by an attention mechanism to assign varying weights, capturing long-term dependencies. An LSTM decoder generates initial trajectory predictions, which are finally refined and constrained by an A* path planning algorithm that ensures physical feasibility and adherence to environmental structures (e.g., roads, obstacles), resulting in smooth and rational future paths.
Real-world Validation
Extensive experiments, including simulations and real-world data collection using custom wearable INS-GNSS devices, confirm the proposed method's superiority. The Gait-AUKF significantly reduces localization errors (e.g., 30% eastward error reduction). The complete prediction framework achieves substantial reductions in Average Displacement Error (68.54%) and Final Displacement Error (70.42%) compared to baselines like LSTM and Transformer models, demonstrating robust performance in diverse urban scenarios, including complex turning maneuvers and overpass crossings.
30%
Reduction in Eastward Error with Gait-AUKF compared to UKF and AKF
Pedestrian Trajectory Prediction Process Flow
Data Source (IMU/GNSS)
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Data Preprocessing (Alignment, Outliers, Attitude)
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Trajectory Position (Gait-AUKF, Pedestrian Gait)
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Trajectory Plan (Map Data, A-Star)
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Trajectory Prediction (Historical Trajectory, Movement Characteristics)
| Method | East (m) | North (m) | Up (m) |
|---|---|---|---|
| UKF | 2.3734 | 2.6592 | 3.2435 |
| AUKF | 2.3259 | 2.4349 | 3.7291 |
| Gait-AUKF | 2.2368 | 2.0628 | 3.1792 |
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Autonomous Path Planning in an Urban Overpass Intersection
The framework successfully models and plans pedestrian movement through a complex urban overpass intersection. By leveraging map data and A* path planning, it directs pedestrians to use the overpass when ground crosswalks are blocked, demonstrating its ability to generate physically feasible paths despite complex environmental constraints.
Impact: This capability is crucial for enhancing pedestrian safety in smart cities by ensuring VRUs follow safe, designated routes. It also validates the model's robustness in handling realistic urban scenarios, providing reliable guidance where traditional GNSS data alone might be inaccurate due to signal interference.
Overall Prediction Framework Performance Improvements
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Reduction in Average Displacement Error (ADE)
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Reduction in Direction Error (DE)
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrating the advanced pedestrian trajectory prediction system into your enterprise. Each step is designed to ensure seamless deployment and maximum impact.
Phase 1: Data Integration & System Setup
Establish secure data pipelines for INS/GNSS input, integrate wearable devices, and configure the Gait-AUKF module. Set up the foundational infrastructure for data processing and real-time localization. (Est. 4-6 Weeks)
Phase 2: Model Training & Customization
Train the GRU-Attention-LSTM prediction model with your specific operational data. Customize the A* path planning algorithm to integrate your enterprise's unique environmental maps and constraints. (Est. 6-8 Weeks)
Phase 3: Pilot Deployment & Validation
Implement the system in a controlled pilot environment. Conduct rigorous testing and validation against real-world scenarios, fine-tuning parameters based on performance metrics (ADE, FDE, RMSE). (Est. 8-10 Weeks)
Phase 4: Full-Scale Rollout & Continuous Optimization
Deploy the system across your entire operational scope. Establish monitoring frameworks for continuous performance evaluation and integrate feedback loops for ongoing model refinement and adaptive learning. (Est. 12+ Weeks)
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