Pedestrian Intent Prediction for Early Collision Warnings
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Solution Overview
Problem
Existing pedestrian protection systems are limited by their reliance on short-term collision detection and lack of context awareness, making them ineffective in dynamic environments and posing risks to pedestrians and vehicle manufacturers.
Innovation Solution
An infrastructure-to-vehicle early warning system that uses road user intent prediction based on Bayesian inference, path planning, optimization, and intelligent infrastructure to predict potential collisions and provide warnings to autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If short-term collision detection is used, then the system is simple to implement, but the reliability of collision prevention is insufficient
Solution Approach 1:
The system performs preliminary action by predicting road user trajectories and detecting potential collisions before they occur. The trajectory prediction module forecasts future positions of pedestrians and cyclists, enabling the system to identify potential conflicts in advance and alert drivers proactively, rather than merely detecting collisions after they happen.
Solution Approach 2:
The system segments the collision detection function into multiple specialized modules: trajectory prediction module, potential collision detection module, and alert generation module. This segmentation allows each module to focus on a specific task, improving overall reliability while maintaining manageable system complexity through modular architecture.
2Reliability
If context awareness is added to pedestrian protection systems, then the reliability improves, but the device complexity increases
Solution Approach 1:
The trajectory prediction module serves multiple functions simultaneously: it predicts future positions of road users, determines their movement intent, identifies potential collision risks, and provides data for alert generation. This multi-functionality improves pedestrian protection reliability without proportionally increasing system complexity, as one module accomplishes what would otherwise require several separate systems.
3Measurement precision
If trajectory prediction is implemented, then the ability to detect potential collisions improves, but the computing resources required increase
Solution Approach 1:
The system applies partial action by focusing trajectory prediction computations only on detected road users (pedestrians and cyclists) rather than all objects in the environment. The potential collision detection module further refines this by only triggering alerts when predicted trajectories indicate actual collision risk, avoiding unnecessary computations and reducing energy consumption while maintaining high detection precision.
Data Source
AI summary
An apparatus comprising a memory to store an observed trajectory of a pedestrian, the observed trajectory comprising a plurality of observed locations of the pedestrian over a first plurality of timesteps; and a processor to generate a predicted trajectory of the pedestrian, the predicted trajectory comprising a plurality of predicted locations of the pedestrian over the first plurality of timesteps and over a second plurality of timesteps occurring after the first plurality of timesteps; determine a likelihood of the predicted trajectory based on a comparison of the plurality of predicted locations of the pedestrian over the first plurality of timesteps and the plurality of observed locations of the pedestrian over the first plurality of timesteps; and responsive to the determined likelihood of the predicted trajectory, provide information associated with the predicted trajectory to a vehicle to warn the vehicle of a potential collision with the pedestrian.