Autonomous Vehicle Control Using Area Collision Risk Index
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Solution Overview
Problem
Existing technologies fail to effectively mitigate vehicle collisions by automatically engaging or disengaging autonomous vehicle control features in hazardous areas, as they lack a systematic method to quantify and compare the riskiness of different areas, leading to unnoticed high-risk zones and inadequate safety measures.
Innovation Solution
The system calculates a collision risk index for areas by analyzing historical traffic data, including insurance claims and sensor data, to determine the likelihood of collisions and automatically engages or disengages autonomous vehicle control features, such as steering and braking, based on this index, and transmits notifications to vehicles approaching hazardous areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicle control features are manually engaged or disengaged, then driver control and system resource usage are optimized, but safety in hazardous areas cannot be automatically ensured
Solution Approach 1:
The system performs preliminary actions by calculating collision risk indices for different geographic areas in advance using historical traffic data, then uses these pre-calculated indices to automatically engage or disengage autonomous vehicle control features when vehicles enter high-risk zones, enabling proactive safety measures without real-time delays
Solution Approach 2:
The system establishes a feedback loop where collision risk indices are continuously calculated from historical data, transmitted to vehicles, and used to automatically adjust autonomous control engagement status. This closed-loop feedback enables the system to adaptively respond to hazardous areas based on accumulated historical collision patterns
2Reliability
If collision risk indices are calculated and transmitted to all vehicles, then safety in hazardous areas is improved, but communication system load and energy consumption increase
Solution Approach 1:
The system applies local quality by calculating and transmitting collision risk indices only for specific geographic areas where collision risks exceed predetermined thresholds, rather than continuously broadcasting risk information for all areas. This targeted approach ensures that communication resources are consumed only when and where safety information is critically needed
3Measurement precision
If historical traffic data is analyzed to identify hazardous areas, then collision risk quantification is achieved, but data processing complexity and time requirements increase
Solution Approach 1:
The system segments the complex task of collision risk analysis by dividing historical traffic data processing into distinct functional modules: data collection from multiple sources, collision identification, risk index calculation, and threshold comparison. This modular segmentation reduces overall system complexity while maintaining comprehensive analysis capabilities
Data Source
AI summary
Systems and methods relate to, inter alia, calculating a collision risk index for an area based upon historical traffic data. The systems and methods may further generate a notification to automatically engage or disengage an autonomous, or semi-autonomous, vehicle control feature in a vehicle based upon the collision risk index for the area. The systems and methods may further transmit the notification to a device of the vehicle to facilitate automatically engaging or disengaging an autonomous, or semi-autonomous, vehicle control feature in the vehicle as the vehicle approaches the area. As a result, vehicle collisions may be reduced, and vehicle safety enhanced.