Autonomous Driver Training Scoring via Sensor Input Comparison
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Solution Overview
Problem
Current methods for training human drivers using autonomous vehicles are inefficient, costly, and prone to biases, as they rely on human instructors, which can be time-consuming and risky.
Innovation Solution
An autonomous vehicle system that measures manual inputs from trainees using sensors and compares them with recommended actions determined by an autonomous driving algorithm, providing feedback and scoring to assess the trainee's performance, allowing gradual control over vehicle features based on scoring thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human instructors are used to train drivers, then personalized guidance and correction can be provided, but the training process becomes time-consuming, costly, and prone to instructor biases
Solution Approach 1:
The patent creates a virtual copy of the driving environment and instructor feedback through software simulation. The training system replicates real driving scenarios, traffic conditions, and instructor evaluation criteria in a virtual space, allowing trainees to practice repeatedly without consuming instructor time while maintaining evaluation accuracy through standardized assessment algorithms.
Solution Approach 2:
The system enables trainees to self-evaluate their performance through automated feedback mechanisms. The software automatically analyzes driving behavior, compares it against optimal patterns, and provides correction guidance without requiring continuous human instructor intervention, thereby reducing training time while maintaining evaluation precision through objective metrics.
2Reliability
If human instructors are used to train drivers, then real-time feedback can be provided, but the system becomes costly and difficult to schedule
Solution Approach 1:
The patent implements an automated feedback loop where the software continuously monitors trainee performance, compares it with optimal driving patterns, and provides immediate corrective feedback. This systematic feedback mechanism ensures consistency across all trainees and training sessions, eliminating the variability and scheduling complexity associated with human instructor availability while maintaining reliable, standardized evaluation.
3Adaptability or versatility
If human instructors are used to train drivers, then nuanced judgment can be applied, but risks and inefficiencies increase
Solution Approach 1:
The training system dynamically adapts to individual trainee needs while maintaining safety standards. The software adjusts training difficulty, scenario selection, and feedback intensity based on real-time performance data, providing flexible personalized training programs. Meanwhile, automated safety monitoring and standardized evaluation protocols ensure consistent safety reliability without the variability and risk associated with human instructor judgment.
Data Source
AI summary
In accordance with an exemplary embodiment, a method is provided for training a trainee using an autonomous vehicle, the method including: measuring, via one or more sensors, one or more manual inputs from the trainee with respect to controlling the autonomous vehicle; determining, via a processor using an autonomous driving algorithm stored in a memory of the autonomous vehicle, one or more recommended actions for the autonomous vehicle; comparing, via the processor, the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle, generating a comparison; and determining, via the processor, a score for the trainee based on the comparison between the one or more manual inputs from the trainee with the one or more recommended actions for the autonomous vehicle.