A Literature Review on Machine Learning-Based Risk Prediction and Decision Support Systems in Autonomous Driving
- 26 July 2025
- book chapter
- Published by Springer Nature
- p. 761-770
Abstract
No abstract availableKeywords
This publication has 28 references indexed in Scilit:
- Does road environment aesthetics influence risky driving behavior of autonomous vehicles? An evaluation on road readiness using explainable machine learning and random parameters multinomial logit with heterogeneityAccident Analysis & Prevention, 2024
- Machine learning for automation usage prediction: identifying critical factors in driver decision-makingApplied Intelligence, 2024
- A comparative analysis of machine learning and statistical methods for evaluating building performance: A systematic review and future benchmarking frameworkBuilding and Environment, 2024
- Deep Reinforcement Learning Based Decision-Making Strategy of Autonomous Vehicle in Highway Uncertain Driving EnvironmentsAutomotive Innovation, 2023
- An integrated lane change prediction model incorporating traffic context based on trajectory dataTransportation Research Part C: Emerging Technologies, 2022
- A personalized lane keeping system for autonomous vehicles based on recurrent neural network with temporal dependenciesJournal of Mechanical Science and Technology, 2022
- Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awarenessTransportation Research Part C: Emerging Technologies, 2022
- An integrated methodology for real-time driving risk status prediction using naturalistic driving dataAccident Analysis & Prevention, 2021
- Machine Learning: Algorithms, Real-World Applications and Research DirectionsSN Computer Science, 2021
- Short-term prediction of safety and operation impacts of lane changes in oscillations with empirical vehicle trajectoriesAccident Analysis & Prevention, 2020