ArticleNature communications2020
Human-like driving behaviour emerges from a risk-based driver model.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Visuomotor adaptation and savings to constant and varying visual feedback delays in a driving simulator.Journal of vision · 2026Article
- From Concept to Representation: Modeling Driving Capability and Task Demand with a Multimodal Large Language Model.Sensors (Basel, Switzerland) · 2025Article
- Empowering safer socially sensitive autonomous vehicles using human-plausible cognitive encoding.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Embodied neuromorphic synergy for lighting-robust machine vision to see in extreme bright.Nature communications · 2024Article
- A model of dyadic merging interactions explains human drivers' behavior from control inputs to decisions.PNAS nexus · 2024Article
- A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations.Sensors (Basel, Switzerland) · 2024Review
- Enhancing Autonomous Vehicle Decision-Making at Intersections in Mixed-Autonomy Traffic: A Comparative Study Using an Explainable Classifier.Sensors (Basel, Switzerland) · 2024Article
- Putting ChatGPT vision (GPT-4V) to the test: risk perception in traffic images.Royal Society open science · 2024Article
- A multimodal physiological dataset for driving behaviour analysis.Scientific data · 2024Article
- Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference.Frontiers in neurorobotics · 2024Article
- Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends.Sensors (Basel, Switzerland) · 2023Review
- Understanding common human driving semantics for autonomous vehicles.Patterns (New York, N.Y.) · 2023Article
- Modelling communication-enabled traffic interactions.Royal Society open science · 2023Article
- Driver models for the definition of safety requirements of automated vehicles in international regulations. Application to motorway driving conditions.Accident; analysis and prevention · 2022Article
- Human injury-based safety decision of automated vehicles.iScience · 2022Article
- Effects of gender, age, experience, and practice on driver reaction and acceptance of traffic jam chauffeur systems.Scientific reports · 2021Article
- Risk Assessment by a Passenger of an Autonomous Vehicle Among Pedestrians: Relationship Between Subjective and Physiological Measures.Frontiers in neuroergonomics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current driving behaviour models are designed for specific scenarios, such as curve driving, obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse scenarios. Can we find an underlying principle from which driving behaviour in different scenarios emerges? We propose the Driver's Risk Field (DRF), a two-dimensional field that represents the driver's belief about the probability of an event occurring. The DRF, when multiplied with the consequence of the event, provides an estimate of the driver's perceived risk. Through human-in-the-loop and computer simulations, we show that human-like driving behaviour emerges when the DRF is coupled to a controller that maintains the perceived risk below a threshold-level. The DRF model predictions concur with driving behaviour reported in literature for seven different scenarios (curve radii, lane widths, obstacle avoidance, roadside furniture, car-following, overtaking, oncoming traffic). We conclude that our generalizable DRF model is scientifically satisfying and has applications in automated vehicles.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.