Telematics - Papers

Datasets

• levin vehicle telematics
• Automobile Telematics Dataset
• Safe Driver Telematics
• Example Driving Data (telematics)
• Synthetic Dataset

Paper - Synthetic Dataset Generation of DriverTelematics

Features

Further Datasets - Link

Paper - Collaborative Cloud-Edge Computation for Personalized Driving Behavior Modeling

Key Notes

• Generative Adversarial Recurrent Neural Networks (GARNN)
• CGARNN-Edge (Conditional GARNN)
• Driving behavior modeling can also be used by insurance companies to determine the vehicle insurance premium
• Drivers may have distinct driving behaviors because of their individual difference, such as age group, gender, and driving experience
• Real-time performance is a stringent requirement for ADAS. For example, fatigue driving or other abnormal driving behaviors should be detected immediately

• Driving behavior: speed, acceleration, brake force, steering, lane offset, and lane position signal

Paper - Driver Telematics Analysis

Key Notes

• Aggressive behaviors include lane violations, failure to stop, speeding, sudden raise of acceleration and severe other violations
• Behavior parameters that account for aggregate driving profile: mean speed, mean speed excluding the stops, mean acceleration, mean deceleration, average length of a trip, mean number of acceleration/deceleration changes within a trip, standstill time proportion, acceleration time proportion, deceleration time proportion and constant speed time proportion
• Trip Features - Ride Length, Ride Speed, Ride Length without stops, Ratio of Stops, speeds, angles, accelerations, speed*angles.
• Driving features - Mean acceleration, Mean deceleration, Average number of acceleration/deceleration changes

Paper - Analyzing driving behavior from CAN data using context-specific information 

Key Notes

Speed based acceleration thresholds - applicable to all categories

• Here adaptive speed - based thresholding is derived exponential regression equations
• Thresholds for turn / straight segments are different. Stricter for turns

Paper - Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification∗

Key Notes

• Input: A trajectory 𝑇 .
• Model: A predictive model 𝑀 to capture variations in driving behavior to derive driving style information.
• Goal: Predict identity of driver for trajectory 𝑇 based on driving style information.
• Optimization Objective: Minimize prediction error

A Vehicle Classification Algorithm based on Telematics Data

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