Helmet Gaze Detection for Predicting Two-Wheeler Motion Intent
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous vehicle systems struggle to accurately predict the motion intentions of vulnerable road users, such as motorcycle riders and bicyclists, due to their dynamic and quick maneuvering capabilities, which are often obscured by a lack of a protective enclosure and reliance on estimating unified boundary extents.
Innovation Solution
The system determines the orientation of a head-worn protective equipment, like helmets, to infer the gaze direction of the rider, using sensors like LiDAR and cameras, and predicts the vehicle's motion path based on this information to control autonomous driving operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses sensor data and unified boundary extents to detect traffic participants, then the system can identify the presence of vulnerable road users, but the accuracy of predicting their motion intentions deteriorates due to their dynamic maneuvering capabilities
Solution Approach 1:
The system segments the detection and prediction process into distinct components: first detecting the vulnerable road user's presence and position using sensor data, then separately analyzing their motion intentions through multiple data sources including sensor data, map data, and trajectory data. This segmentation allows each component to be optimized independently, improving overall prediction reliability while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and preprocessing sensor data, map data, and trajectory data before motion intention prediction is needed. This includes pre-identifying vulnerable road users and their current states, so that when prediction is required, the system can quickly generate accurate predictions using already-processed information, thereby improving reliability without compromising detection precision.
2Loss of information
If the system focuses on detecting vulnerable road users without protective enclosures, then the system can identify their presence, but the ability to accurately predict their actions deteriorates due to lack of enclosed structure references
Solution Approach 1:
The system uses a universal detection framework that handles both enclosed vehicles and vulnerable road users with protective enclosures through the same sensor data processing pipeline. This multi-functional approach allows the system to identify all traffic participants uniformly while applying specialized prediction algorithms for vulnerable road users based on their unique characteristics such as trajectory patterns and environmental context, rather than relying on structural references.
Solution Approach 2:
The system changes the parameters used for prediction when detecting vulnerable road users. Instead of relying on structural references from enclosed vehicles, the system switches to parameters such as trajectory data, map data, environmental context, and temporal patterns specific to vulnerable road users. This parameter adaptation enables accurate prediction of their intended actions despite the lack of protective enclosures.
3Ease of operation
If the system processes sensor data for all traffic participants uniformly, then the system maintains consistent detection methods, but the speed of predicting motions of quick maneuvering vehicles deteriorates
Solution Approach 1:
The system implements dynamic processing that adapts to the characteristics of different traffic participants. While maintaining a uniform detection framework for consistency, the system dynamically adjusts the prediction algorithms and data sources based on the detected object type. For vulnerable road users and quick maneuvering vehicles, the system activates specialized fast-processing modes that prioritize trajectory data and environmental context, thereby improving prediction speed without sacrificing detection method consistency.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems, methods, and autonomous vehicles may obtain one or more images associated with an environment surrounding an autonomous vehicle; determine, based on the one or more images, an orientation of a head worn item of protective equipment of an operator of a vehicle; determine, based on the orientation of the head worn item of protective equipment, a direction of a gaze of the operator and a time period associated with the direction of the gaze of the operator; determine, based on the direction of the gaze of the operator and the time period associated with the direction of the gaze of the operator, a predicted motion path of the vehicle; and control, based on the predicted motion path of the vehicle, at least one autonomous driving operation of the autonomous vehicle.