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Attribution Theory

Attribution Theory

Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).

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Related Experiment Video

Updated: May 27, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

Multi-scenario driving style research based on driving behavior pattern extraction.

Yi He¹, Yingrui Hu¹, Jipu Li¹

¹Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China; Engineering Research Center of Transportation Information and Safety, Ministry of Education, Wuhan 430206, China.

Accident; Analysis and Prevention

|February 18, 2025

Summary

This summary is machine-generated.

This study introduces a new framework to analyze driving styles using electric vehicle data. It identifies distinct driving behavior patterns and categorizes drivers, improving road safety and intelligent driving systems.

Keywords:

Automobile driving Driving patterns Driving style Electric vehicle Semi-hidden Markov model

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Area of Science:

Transportation Science
Intelligent Transportation Systems
Data Science

Background:

Accurate analysis of driver behavior is vital for road safety and advanced driving systems.
Existing research often overlooks detailed driving sequences and environmental influences on driving styles.

Purpose of the Study:

To propose a novel framework for driving style analysis using natural driving data.
To extract and categorize driving behavior patterns considering different driving scenarios and environments.
To validate the extracted patterns using energy consumption data and quantify driver differences.

Main Methods:

Utilized natural driving data from electric vehicles in Wuhan.
Employed kernel density estimation and relative entropy to verify driving feature convergence.
Applied Hierarchical Dirichlet Process Hidden Semi-Markov Model (HDP-HSMM) and K-means clustering for pattern extraction.
Used Jensen-Shannon (JS) divergence to quantify behavioral differences among drivers.

Main Results:

Extracted 4 and 5 distinct driving behavior patterns for free-driving and car-following scenarios, respectively.
Validated patterns by linking them to energy consumption distribution, identifying high consumption in aggressive acceleration and steady-state high-speed driving.
Categorized drivers into aggressive, moderate, and conservative types based on quantified behavioral differences.
Demonstrated that driving environments influence driving styles, leading to variations across scenarios.

Conclusions:

The proposed framework effectively extracts and categorizes driving behavior patterns.
Driving style analysis is enhanced by considering driving sequences, environments, and energy consumption.
Findings contribute to improved road safety and the development of more sophisticated intelligent driving systems.