LTE Pedestrian Collision Avoidance Using AI Trajectory Prediction
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Solution Overview
Problem
Current vehicle-to-pedestrian collision avoidance systems lack the necessary precision and accuracy, with existing LTE and GPS technologies providing positioning accuracies of tens of meters, which is insufficient for distinguishing between pedestrians in different scenarios, leading to potential safety limitations.
Innovation Solution
A method and system utilizing LTE-capable user equipment terminals linked to vehicles and pedestrians, with a Location Service Client server employing a Recurrent Neural Network algorithm for spatiotemporal positioning analysis and a Conditional Random Fields algorithm to predict future trajectories, communicating collision-avoidance signals when proximity thresholds are met, leveraging 5G NR technology for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS is used for positioning, then positioning accuracy is improved to 5 meters, but measurement latency increases to seconds level and battery consumption increases
Solution Approach 1:
The patent combines multiple positioning techniques (Cell ID, RSSI, TDOA, AOA, TOA) into a hybrid positioning system that leverages the strengths of each method while mitigating their individual weaknesses, achieving both accuracy and reduced latency
Solution Approach 2:
The patent introduces a Location Service Client server as an intermediary that processes positioning data from multiple sources and algorithms, coordinating the complex positioning operations and providing unified location information to reduce processing latency
2Ease of manufacture
If Cell ID technique is used for positioning, then implementation simplicity is improved, but positioning accuracy deteriorates to tens of meters due to large serving cell radius
Solution Approach 1:
The patent segments the positioning task by using multiple algorithms simultaneously - Cell ID provides coarse location for simplicity while RSSI, TDOA, and other methods provide fine-grained accuracy, with the Location Service Client integrating these segmented results
Solution Approach 2:
The patent changes the positioning parameters dynamically by selecting different algorithms based on availability and accuracy requirements, adjusting the balance between simplicity and precision in real-time
3Measurement precision
If RSSI triangulation is used for positioning, then positioning accuracy is improved, but system complexity increases due to signal measurement and calculation requirements
Solution Approach 1:
The Location Service Client server acts as an intermediary that handles the complex RSSI measurements and triangulation calculations, moving the computational complexity from the user device to the server while maintaining high positioning accuracy
Solution Approach 2:
The patent replaces complex manual signal analysis with automated algorithms (RSSI, TDOA, AOA, TOA) that the Location Service Client processes automatically, reducing the operational complexity at the user end
Data Source
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AI summary
A method and a system for vehicle-to-pedestrian collision avoidance system, the system comprising participants consisting of Long-Term Evolution (LTE)-capable user equipment (UE) terminals physically linked to at least one vehicle and at least one pedestrian; wherein a spatiotemporal positioning of the terminals is determined from Long Term Evolution (LTE) cellular radio signals mediated by Long-Term Evolution (LTE) cellular base stations (BS) and a Location Service Client (LCS) server including an embedded Artificial Intelligence algorithm comprising a Recurrent Neural Network (RNN) algorithm and analyzes the spatiotemporal positioning of the terminals and determines the likely future trajectory and communicates the likely future trajectory of the participants to the terminals physically linked to the pedestrian; the terminals physically linked to the pedestrian include an embedded Artificial Intelligence algorithm comprising a Conditional Random Fields (CRFs) algorithm to determine if the likely future trajectory of the pedestrian is below a vehicle-to-pedestrian proximity threshold limit and, if this condition is reached, communicates a collision-avoidance emergency signal to the at least one pedestrian and/or vehicle that meet the proximity threshold limit.