Autonomous Vehicle Parameter Adjustment via Passenger Feedback
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Solution Overview
Problem
Current methods for adjusting autonomous vehicle parameters rely on human programmers, which are costly, challenging to scale, and introduce subjective elements, failing to account for all possible situations and passenger preferences effectively.
Innovation Solution
A computer-implemented method using a machine-learned model that adjusts vehicle parameters based on passenger feedback and vehicle data logs, allowing for improved passenger experiences by associating passenger input data with vehicle operating parameters and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human programmers manually adjust autonomous vehicle parameters, then subjective expertise and flexibility are applied, but costs increase and scalability decreases
Solution Approach 1:
The system enables autonomous vehicles to self-adjust their operating parameters by automatically processing passenger feedback and vehicle data logs through a machine-learned model, eliminating the need for human programmer intervention and enabling scalable deployment across the fleet
Solution Approach 2:
The system implements a closed-loop feedback mechanism where passenger experiences are collected, processed through machine learning models, and used to automatically adjust vehicle parameters, creating a continuous improvement cycle that scales without additional human resources
2Adaptability or versatility
If human programmers adjust vehicle parameters, then subjective elements are introduced, but consistency and objectivity decrease
Solution Approach 1:
The system replaces human subjectivity with automated machine learning models that objectively process passenger feedback and vehicle data, ensuring consistent and reproducible parameter adjustments across all vehicles without human bias
Solution Approach 2:
The machine-learned model automatically determines optimal parameter adjustments based on processed feedback data, transforming subjective passenger experiences into precise, consistent quantitative parameter changes that can be reliably replicated
3Reliability
If manual parameter adjustment methods are used, then human expertise is applied, but cost and time consumption increase
Solution Approach 1:
The system automates the entire parameter adjustment workflow from feedback collection to implementation, eliminating manual review and decision-making time while maintaining or improving adjustment accuracy through machine learning
Solution Approach 2:
The machine-learned model is pre-trained on historical data to quickly determine optimal parameter adjustments in real-time, eliminating the need for time-consuming manual analysis while ensuring reliable and accurate parameter settings
Data Source
AI summary
Systems and methods for adjusting autonomous vehicle parameters in response to passenger feedback are provided. A method can include obtaining, by a computing system comprising one or more computing devices, passenger input data descriptive of one or more passenger experiences associated with one or more autonomous driving sessions, obtaining, by the computing system, one or more vehicle data logs descriptive of vehicle conditions associated with the passenger input data descriptive of one or more passenger experiences associated with the one or more autonomous driving sessions, determining, by the computing system using a machine-learned model, an adjusted vehicle parameter of an autonomous vehicle based at least in part on the passenger input data descriptive of one or more passenger experiences and the associated one or more vehicle data logs descriptive of vehicle conditions, and implementing, by the computing system, the adjusted vehicle parameter in an operation of the autonomous vehicle.