Spatio-Temporal Driving Skill Models for Ride-Sharing Safety
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Solution Overview
Problem
Existing carpooling technologies fail to account for driver skill variations and anomalous behavior across different landscapes, which can impact passenger safety and trip efficiency.
Innovation Solution
A cognitive-based system that generates spatio-temporal driving skill models for each user, monitors ride-sharing trip data, detects driving anomalies, and updates the trip schedule by adjusting driver assignments and routes based on detected anomalies and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing carpooling technology is used, then ride-sharing trips can be organized, but driver skill variations and anomalous behavior are not accounted for, reducing passenger safety
Solution Approach 1:
The system performs preliminary actions by generating spatio-temporal driving skill models for each user before ride-sharing trips occur. These models are created based on historical driving data and landscape information, enabling the system to assess driver competence in advance and make informed driver assignments that prioritize passenger safety without adding operational complexity during actual trips
Solution Approach 2:
The system segments driver evaluation into landscape-specific components by creating separate driving skill models for different geographic regions and road types. This segmentation allows the system to account for varying driver competencies across different environments, improving safety assessments while maintaining manageable system complexity through modular model structures
2Reliability
If driver assignments are optimized based on driving skills, then passenger safety improves, but trip scheduling complexity increases
Solution Approach 1:
The system changes parameters by incorporating landscape-specific driving skill metrics into the scheduling optimization process. Rather than using simple driver availability, the system optimizes assignments based on matched parameters between driver skill profiles and landscape requirements, achieving improved safety through parameter-based optimization that integrates seamlessly with existing scheduling frameworks
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual driving performance during trips and comparing it against the spatio-temporal driving skill models. This feedback loop allows the system to refine driver assignments and update skill models over time, improving safety outcomes while using the same feedback infrastructure that already exists in ride-sharing operations for quality control
3Reliability
If anomaly detection is implemented, then driving safety improves, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by implementing anomaly detection that focuses specifically on landscape-relevant behaviors rather than monitoring all possible driving parameters. The spatio-temporal models only track driving patterns pertinent to specific geographic regions and road types, reducing computational energy requirements while maintaining driving safety through targeted anomaly detection of the most critical risk factors
4Measurement precision
If landscape-specific driving models are created, then driver skill assessment accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential landscape-related features from driving data to create spatio-temporal models, rather than processing complete raw datasets. By extracting specific parameters such as speed variations, acceleration patterns, and positioning data relevant to particular landscapes, the system achieves high measurement precision for driver skill assessment while minimizing data processing requirements through selective feature extraction
Data Source
AI summary
Methods, systems, and computer program products for driving anomaly detection based on spatio-temporal landscape-specific driving models are provided herein. A method includes generating, for each of multiple users, a temporally-related driving skill model pertaining to one or more landscapes, wherein the model is based on temporally-related driving data associated with the users and landscape-related information of trips driven by the users; monitoring the users participating in a ride-sharing trip in a vehicle by analyzing ride-sharing trip data; detecting driving-related anomalies attributed to the monitored users by comparing the ride-sharing trip data and the respective temporally-related driving skill model for each monitored user; updating a schedule for the trip based on the detected anomalies and estimated conditions attributed to remaining portions of the trip by modifying an assignment of selected users to drive the vehicle during the remaining portions of the trip; and outputting the updated schedule to the selected users.