Vehicle Monitoring for Blind Spot Lane Change Prediction
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Solution Overview
Problem
Existing vehicle monitoring systems often fail to adequately alert drivers to potential lane changes by surrounding vehicles, especially when turn signals are not used or when vehicles are in blind spots, leading to safety risks on roadways.
Innovation Solution
A monitoring system equipped with sensors, navigation data, and a controller that processes data to predict lane changes and issue alerts, utilizing blind spot monitoring, occupant monitoring, and crowdsourced data to enhance safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blind spot monitoring is implemented to detect surrounding vehicles, then safety is improved, but false alerts may occur due to unpredictable driver behaviors
Solution Approach 1:
The system performs preliminary monitoring of lane change behaviors and occupant actions before issuing alerts. By continuously tracking patterns in advance and building a behavioral score, the system can distinguish between normal and potentially dangerous behaviors, reducing false alerts while maintaining safety.
Solution Approach 2:
The system uses feedback from multiple sensors (image sensors, blind spot monitoring, occupant monitoring) and crowdsourced data to continuously refine its predictions. The behavioral score provides feedback that helps the system learn from past behaviors and improve alert accuracy over time.
2Measurement precision
If multiple sensors and monitoring functions are added to predict lane changes, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The monitoring application serves multiple functions: it monitors blind spots, tracks lane change behaviors, detects occupant actions, processes crowdsourced data, and generates predictions. By making the system multi-functional, the patent avoids adding separate dedicated systems for each function, thereby managing complexity while improving prediction accuracy.
Solution Approach 2:
The patent combines multiple data sources (sensor data, navigation data, crowdsourced data) and monitoring functions into a unified monitoring application. This consolidation allows the system to leverage synergies between different data types and functions, improving prediction accuracy without proportionally increasing complexity.
3Reliability
If real-time monitoring of lane change behaviors is implemented, then road safety is enhanced, but data processing requirements and time consumption increase
Solution Approach 1:
The system continuously collects and pre-processes lane change behavior data in the background, building behavioral profiles and scores before alerts are needed. This preliminary processing reduces the computational burden during critical decision-making moments, enabling fast response times while maintaining comprehensive safety monitoring.
Solution Approach 2:
The monitoring application autonomously processes data from multiple sources and generates predictions without requiring real-time human intervention. The system self-manages the complex data processing tasks, filtering and analyzing information continuously to provide timely safety alerts without adding manual processing time.
Data Source
AI summary
A monitoring system for a vehicle includes data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that when executed on the data processing hardware cause the data processing hardware to perform operations. The operations include monitoring, via a monitoring application for a vehicle, one or more secondary vehicles, capturing, via an image sensor, occupant data and blind spot data, and receiving, via a back-office server, crowdsourced data. The operations also include monitoring, via the monitoring application, the occupant data, the blind spot data, and the crowdsourced data, generating, based on the occupant data, the blind spot data, and the crowdsourced data, a score via a scoring function of the monitoring application, and generating, via a prediction function of the monitoring application, a prediction notification based on the score.