Vehicular Multi-Sensor Sensing for Context-Aware Hazard Alerts
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Solution Overview
Problem
Existing vehicle imaging systems often provide unnecessary warnings due to the particular driving conditions or environment, failing to adjust processing based on context, leading to driver distraction and inefficiency.
Innovation Solution
A vehicle vision system that utilizes cameras and wireless communication to detect objects, adjust processing modes based on context, and provide alerts or interventions only when necessary, incorporating data from remote sources to enhance hazard anticipation and reduce false alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes all captured image data in detail, then detection precision is improved, but processing time increases and false alerts increase
Solution Approach 1:
The system dynamically adjusts processing intensity based on detected context. When wireless communication indicates a hazard condition, the system switches to high-processing mode with focused analysis on relevant regions. When no hazards are detected, it operates in low-processing mode with reduced analysis, thereby reducing overall processing time while maintaining detection precision when needed.
Solution Approach 2:
The system applies different processing quality levels to different regions of the image data. Full-resolution processing is applied only to regions identified as potentially containing hazards (based on wireless communication data and initial sensor detection), while other regions receive minimal or no processing. This localized high-quality processing maintains detection precision for critical areas while reducing overall processing time.
2Reliability
If the system provides frequent alerts to ensure safety, then driver safety is improved, but driver distraction increases
Solution Approach 1:
The system uses feedback from wireless communications and sensor data to intelligently determine when alerts are necessary. By continuously monitoring contextual information (traffic conditions, road conditions, detected objects), the system provides alerts only when actual hazards are present, rather than providing frequent unnecessary alerts. This feedback-driven approach maintains driver safety while reducing driver distraction from false alerts.
Solution Approach 2:
The system performs preliminary analysis of wireless communication data and sensor inputs to identify potential hazards before they become critical threats. By detecting hazards early and providing targeted alerts only when necessary, the system ensures driver safety through timely warnings while avoiding distraction from unnecessary alerts about non-hazardous conditions.
3Reliability
If the system processes data from multiple sensors simultaneously, then detection reliability is improved, but system complexity increases
Solution Approach 1:
The system merges data from wireless communications, object detection sensors, and image processing into a unified contextual understanding. By integrating these diverse data sources and processing them together rather than separately, the system achieves reliable hazard detection while managing complexity through unified processing architecture rather than multiple independent systems.
4Measurement precision
If the system uses extensive image processing to reduce false alerts, then alert accuracy is improved, but processing capacity requirements increase
Solution Approach 1:
The system performs preliminary filtering using wireless communication data and basic sensor detection before applying extensive image processing. By pre-identifying regions of interest and potential hazards using low-cost data sources, the system reduces the amount of image data that requires intensive processing, thereby achieving high alert accuracy while reducing overall processing capacity requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively reduces unnecessary warnings by context-dependent processing, enhancing hazard detection and anticipation, and minimizing false alerts through efficient use of processing capacity and data from remote sources.
Implementation Method 1
car-to-car communication using time of flight (TOF) systems for enhanced hazard anticipation and avoidance
Data Source
AI summary
A vehicular multi-sensor sensing system includes a plurality of sensors disposed at an equipped vehicle and sensing forward of the vehicle. Data based at least in part on processing of sensor data captured by at least one of the sensors is wirelessly transmitted from the equipped vehicle to a remote server. The wirelessly transmitted data is conjuncted to a current geographic location of the equipped vehicle along a road being travelled by the equipped vehicle. The remote server merges data wirelessly transmitted from the equipped vehicle to the remote server with data wirelessly transmitted from a plurality of other vehicles traveling along the road along which the equipped vehicle is traveling, and processes the merged data to determine at least one characteristic along the road being travelled by the equipped vehicle.