Roadmanship System Dynamic Thresholds Human Driving Norms
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Solution Overview
Problem
Advanced driver-assistance systems (ADAS) in vehicles often prioritize safety but fail to effectively 'blend' into everyday traffic, lacking the courtesy and social norms that human drivers exhibit, known as roadmanship.
Innovation Solution
A roadmanship system that uses a computational device and vehicle control system to receive driving data, calculate regression curves, and determine threshold values for vehicle maneuvers based on safety and predetermined roadmanship levels, ensuring vehicles perform maneuvers that mimic human driving behavior in terms of courtesy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driver assist systems prioritize safety parameters, then collision avoidance and safety are improved, but the systems fail to blend into everyday traffic and lack roadmanship
Solution Approach 1:
The system changes parameters by collecting real-world driving data from multiple vehicles and using regression analysis to determine optimal threshold values for engineering parameters. This transforms safety-critical systems from using fixed conservative thresholds to dynamically adjusted thresholds that reflect actual human driving behavior patterns, thereby improving roadmanship while maintaining safety
Solution Approach 2:
The system implements feedback by collecting driving data from groups of vehicles, analyzing it through regression curves, and using the results to refine threshold values for vehicle maneuvers. This continuous feedback loop enables the system to learn from real-world outcomes and adjust its behavior to better match human roadmanship norms
2Reliability
If driver assist systems use fixed safety thresholds, then safety is ensured, but the systems do not mimic human driving behavior patterns
Solution Approach 1:
The system applies dynamics by transitioning from fixed safety thresholds to dynamic thresholds that adapt based on collected driving data and regression analysis. The threshold values are no longer static but are determined through ongoing analysis of human driving patterns, allowing the system to mimic natural driving behavior while maintaining safety through data-driven decision-making
3Reliability
If driver assist systems are overly conservative, then safety is maximized, but the systems create unnecessary hazards for other road users
Solution Approach 1:
The system uses copying by collecting and analyzing actual human driving behavior data from multiple vehicles. Instead of relying on theoretical safety models, the system copies real-world driving patterns and uses regression analysis to identify natural threshold values, thereby mimicking human roadmanship and reducing hazards to other road users while maintaining safety
Data Source
AI summary
A roadmanship system comprises a computational device and a vehicle comprising a plurality of sensors and a vehicle control system in communication with the computational device and the plurality of sensors. The computational device can be configured to: (i) receive driving data from a group of vehicles; (ii) calculate a regression curve based on the driving data; (iii) calculate a threshold value of an engineering parameter based on the regression curve and a predetermined roadmanship level; and (iv) output the threshold value to the vehicle control system. The vehicle control system can be configured to: (a) receive the threshold value from the computational device; (b) receive operational information associated with at least one of the vehicle and a driving environment surrounding the vehicle from the plurality of sensors; and (c) cause the vehicle to perform a vehicle maneuver based on the threshold value and the operational information.