Driver Coaching Video Curation for Non-Collision Event Detection
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Solution Overview
Problem
Legacy driver monitoring systems, relying on inertial sensors, struggle to detect non-collision driving events effectively and overwhelm safety managers with excessive video data, leading to inefficient coaching and potential coaching biases due to human review bottlenecks and false alarms.
Innovation Solution
A system utilizing camera sensors and on-device visual processing to detect and curate non-collision driving events, determining coachable examples, and presenting relevant data to drivers, thereby reducing false alarms and improving data bandwidth utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video processing based systems are used to detect driving events, then the number and range of detected driving events increases, but the amount of data overwhelms safety management and coaching resources
Solution Approach 1:
The patent extracts and transmits only the most relevant driving event data to remote servers, filtering out redundant information. The system identifies critical events based on severity thresholds and selectively uploads only those events that require human review, thereby reducing the overall data volume transmitted and processed remotely while maintaining detection capabilities.
Solution Approach 2:
The patent segments driving events into different categories based on their importance and characteristics. By classifying events into tiers (e.g., critical, moderate, minor), the system can apply different processing and transmission strategies to each segment, reducing the burden on remote coaching resources while preserving essential information.
2Reliability
If all detected driving events are reviewed by human operators, then comprehensive monitoring is achieved, but coaching resources are overwhelmed and efficiency decreases
Solution Approach 1:
The patent applies local quality by implementing intelligent filtering at the local vehicle level before data transmission. The onboard system automatically assesses each driving event's significance and selectively transmits only high-priority events to remote servers, ensuring that human operators receive a curated subset of data that requires their expertise, thereby improving coaching efficiency without compromising monitoring reliability.
Solution Approach 2:
The patent introduces an intermediary intelligent filtering system that sits between the driving event detection and human review processes. This intermediary automatically pre-processes and prioritizes events, acting as a mediator that reduces the volume of data requiring human attention while ensuring that critical events are not missed, thus balancing monitoring completeness with coaching efficiency.
3Measurement precision
If inertial sensor based systems are used, then false alarms are reduced, but certain common driving events that do not produce large inertial readings are undetected
Solution Approach 1:
The patent merges multiple detection approaches by combining inertial sensor data with video processing capabilities. This hybrid system leverages the high accuracy of inertial sensors for detecting significant physical events while supplementing them with video-based detection for events that may not produce strong inertial signals, such as gradual speed changes or subtle driving behaviors, thereby achieving both precision and comprehensive coverage.
Solution Approach 2:
The patent implements a multi-functional detection system that can identify various types of driving events through multiple sensing modalities. The system is designed to handle both inertial-based events (collisions, hard braking) and video-based events (speeding, lane deviations), creating a universal detection framework that adapts to different event types without requiring separate specialized systems.
Data Source
AI summary
Systems and methods for curating video and other driving-related data for use in driver coaching, which may include selecting or ranking driving behaviors for coaching, selecting or ranking drivers for coaching, selecting or ranking video and other data to be used in coaching, preparing for, scheduling, and summarizing coaching sessions, matching the format of coaching to the behavior or person being coached, preventing unsafe driving situations, influencing job dispatch decisions based on safety scores, and/or reducing data bandwidth usage based on a determined coaching effectiveness of video data.