Driver Intention Detection Using Weighted Multi-Cue Fusion
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Solution Overview
Problem
Current object detection systems in vehicles lack the ability to accurately determine a driver's intention to make a left or right turn, especially at intersections, which can lead to collisions due to judgment errors during turning maneuvers.
Innovation Solution
A system and method that combines various cues such as vehicle position, sensor data, GPS, map information, and V2V communications to determine the confidence level of a driver's intention to turn, using algorithms that weight these cues based on current operating conditions and self-learning from prior events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection systems use traditional sensors and algorithms to detect vehicles and objects, then basic collision avoidance is achieved, but the system cannot accurately determine driver intention to turn left or right at intersections
Solution Approach 1:
The system segments driver intention detection into multiple independent cue sources (vehicle position, sensor data, GPS, map information, V2V communications) that are processed separately and then integrated. This allows the complex detection task to be divided into manageable components, improving accuracy without overwhelming system complexity.
Solution Approach 2:
The system merges multiple cue sources and data streams into a unified driver intention determination process. By combining information from diverse sources (sensors, GPS, maps, V2V) and weighting them according to current operating conditions, the system achieves accurate intention detection while managing complexity through integrated processing.
2Measurement precision
If the system uses multiple cue sources and weighted algorithms to determine driver intention, then detection accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The system dynamically adjusts the weighting of different cues based on current operating conditions and continuously updates the confidence level as new data becomes available. This dynamic approach allows the system to reach accurate conclusions faster by adapting to changing situations rather than processing all cues with equal weight regardless of context.
Solution Approach 2:
The system incorporates feedback mechanisms where the determined confidence level is continuously updated and compared against thresholds to trigger appropriate actions. This feedback loop allows the system to refine its intention determination over time and make timely decisions based on accumulating evidence from multiple cues.
3Reliability
If the system provides timely warnings and automatic actions to prevent collisions, then safety improves, but the system may generate false alarms or unnecessary interventions
Solution Approach 1:
The system performs preliminary assessment of driver intention by analyzing multiple cues and determining a confidence level before triggering warnings or automatic actions. This preliminary evaluation ensures that interventions are based on well-substantiated intention detection rather than reacting to every detected anomaly, reducing false alarms while maintaining timely safety responses.
Solution Approach 2:
The system replaces traditional mechanical reaction-based collision avoidance with an intelligent decision-making process that uses algorithms to weigh multiple cues and determine driver intention. This substitution allows for more nuanced judgment of when intervention is truly necessary, reducing false alarms while improving the reliability of actual safety interventions.
Data Source
AI summary
A system and method for determining whether a driver of a host vehicle intends to make a left or right turn with a certain level of confidence. The method obtains a plurality of turning cues that identify external parameters around the host vehicle and/or define operating conditions of the host vehicle, and determines a confidence level that the host vehicle will make a left or right turn based on the turning cues, where determining the confidence level includes weighting each of the cues based on current vehicle operating conditions.