Autonomous Vehicle Traffic Flow via Passenger Sentiment Clustering
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Solution Overview
Problem
Current autonomous vehicle traffic management systems fail to adequately ensure passenger safety and comfort by not considering passenger perception and judgment, leading to potential interference with the vehicle's control systems.
Innovation Solution
An IoT protocol that monitors passenger sentiment data through sensors to determine vehicle clustering and adjust traffic flow, ensuring each vehicle passes through intersections at a rate that maintains passenger comfort and safety levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicles travel through intersections at higher speeds to improve productivity, then the traffic flow efficiency increases, but passenger safety and comfort deteriorate
Solution Approach 1:
The system dynamically adjusts vehicle speed and traffic flow rates based on real-time sentiment data from passengers. Vehicles can transition between different speed regimes and traffic flow patterns depending on the aggregated comfort and safety perceptions of passengers, allowing the system to optimize both productivity and reliability adaptively
Solution Approach 2:
The system implements a feedback loop where passenger sentiment data is continuously collected through sensors, processed to determine clustering patterns, and used to adjust traffic flow management. This closed-loop control ensures that traffic flow decisions are continuously refined based on actual passenger responses, balancing efficiency with safety and comfort
2Reliability
If autonomous vehicles monitor and respond to passenger sentiment data in real-time, then passenger safety and comfort improve, but system complexity increases
Solution Approach 1:
The system uses a unified sentiment analysis framework that processes multiple types of passenger feedback (comfort, safety, confidence) through a single clustering mechanism. This multi-functional approach allows the same system infrastructure to handle various aspects of passenger sentiment, reducing overall complexity compared to separate systems for each type of monitoring
Solution Approach 2:
The patent introduces sentiment clustering as an intermediary layer between raw sensor data and traffic flow control decisions. This intermediate processing stage aggregates and interprets passenger feedback into meaningful clusters that can directly inform traffic management, simplifying the connection between complex sensor inputs and control outputs
3Ease of operation
If autonomous vehicles adjust traffic flow based on passenger sentiment clustering, then passenger comfort is maintained, but traffic flow efficiency may deteriorate
Solution Approach 1:
The system changes traffic flow parameters (speed, spacing, timing) based on sentiment cluster identification. When clusters indicate high comfort levels, the system can maintain or increase efficiency. When clusters indicate discomfort or safety concerns, the system adjusts parameters to improve passenger experience, creating a dynamic balance between comfort and efficiency
4Reliability
If autonomous vehicles reduce speed to maintain passenger comfort levels, then passenger safety improves, but productivity decreases
Solution Approach 1:
The system implements periodic assessment of passenger sentiment through scheduled sensor monitoring and clustering analysis. Traffic flow adjustments are made in periodic cycles based on these assessments, allowing the system to maintain higher speeds during periods of positive sentiment while reducing speed only when necessary, thus balancing safety with productivity
Data Source
AI summary
An autonomous computing system managed through an internet of things (IoT) protocol is provided. A computing device determines a clustering of autonomous vehicles based on expression sentiment data of each passenger of a respective autonomous vehicle. A computing device determines a node within an IoT network and monitors a plurality of autonomous vehicles traveling though the node. A computing device identifies a time frame in which each respective vehicle in the cluster is predicted to pass through an intersection. A computing device identifies a current traffic pattern of a vehicle that is external to the cluster of autonomous vehicles. The one or more processors adjust the traffic flow to allow the clustering of autonomous vehicles to pass through an intersection at a rate that maintains a threshold level of comfort for each passenger in the determined cluster of autonomous vehicles.