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Contributory factors to the choice of active avoidance behavior based on trajectory data

https://doi.org/10.2139/ssrn.5482432
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Outgoing claims: the papers this work cites, builds on, or argues with.

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An active safety control method of collision avoidance for intelligent connected vehicle based on driving risk perception
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Effectiveness of forward collision warning and autonomous emergency braking systems in reducing front-to-rear crash rates
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Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data
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Full Bayesian conflict-based models for real time safety evaluation of signalized intersections
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Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data
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Prediction of rear-end conflict frequency using multiple-location traffic parameters
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Steering or braking avoidance response in SHRP2 rear-end crashes and near-crashes: A decision tree approach
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The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis
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Effectiveness of resampling methods in coping with imbalanced crash data: Crash type analysis and predictive modeling
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Application of explainable machine learning for real-time safety analysis toward a connected vehicle environment
inferredcites →https://doi.org/10.1016/j.aap.2022.106759openalex-snapshot
Caught in the blind spot of a truck: A choice model on driver glance behavior towards cyclists at intersections
inferredcites →https://doi.org/10.1016/j.aap.2023.107088openalex-snapshot
Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections
inferredcites →https://doi.org/10.1016/j.amar.2021.100159openalex-snapshot
Modeling accident risks in different lane-changing behavioral patterns
inferredcites →https://doi.org/10.1016/j.amar.2022.100265openalex-snapshot
An empirical investigation of driver car-following risk evolution using naturistic driving data and random parameters multinomial logit model with heterogeneity in means and variances
inferredcites →https://doi.org/10.1016/j.eswa.2016.12.018openalex-snapshot
Detection of traffic congestion and incidents from GPS trace analysis
inferredcites →https://doi.org/10.1016/j.jesp.2014.01.005openalex-snapshot
The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle
inferredcites →https://doi.org/10.1016/j.trc.2016.08.014openalex-snapshot
Drivers’ rear end collision avoidance behaviors under different levels of situational urgency
inferredcites →https://doi.org/10.1016/j.trc.2019.05.024openalex-snapshot
Examining lane change gap acceptance, duration and impact using naturalistic driving data
inferredcites →https://doi.org/10.1016/j.trf.2016.11.006openalex-snapshot
Driver brake vs. steer response to sudden forward collision scenario in manual and automated driving modes
inferredcites →https://doi.org/10.1016/j.trf.2021.04.002openalex-snapshot
The role of drivers’ social interactions in their driving behavior: Empirical evidence and implications for car-following and traffic flow
inferredcites →https://doi.org/10.1038/s41467-020-18353-4openalex-snapshot
Human-like driving behaviour emerges from a risk-based driver model
inferredcites →https://doi.org/10.1049/iet-its.2020.0355openalex-snapshot
Bézier curve‐based trajectory planning for autonomous vehicles with collision avoidance
inferredcites →https://doi.org/10.1076/vesd.36.2.203.3557openalex-snapshot
Human-Centered Design of an Acc-With-Braking and Forward-Crash-Warning System
inferredcites →https://doi.org/10.1080/00423114.2018.1535125openalex-snapshot
Unified decision-making and control for highway collision avoidance using active front steer and individual wheel torque control
inferredcites →https://doi.org/10.1080/15472450.2020.1834392openalex-snapshot
Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections
inferredcites →https://doi.org/10.1109/25.966586openalex-snapshot
Strategies and spacing requirements for lane changing and merging in automated highway systems
inferredcites →https://doi.org/10.1109/tits.2006.888606openalex-snapshot
Toward Autonomous Collision Avoidance by Steering
inferredcites →https://doi.org/10.1109/tits.2020.2988303openalex-snapshot
Autonomous Driving Learning Preference of Collision Avoidance Maneuvers
inferredcites →https://doi.org/10.1109/tits.2020.3037927openalex-snapshot
Active Collision Avoidance Strategy Considering Motion Uncertainty of the pedestrian
inferredcites →https://doi.org/10.1109/tits.2021.3082944openalex-snapshot
An Automatic Vehicle Avoidance Control Model for Dangerous Lane-Changing Behavior
inferredcites →https://doi.org/10.1109/tits.2023.3273572openalex-snapshot
Brain-Inspired Modeling and Decision-Making for Human-Like Autonomous Driving in Mixed Traffic Environment
inferredcites →https://doi.org/10.1177/0361198119840617openalex-snapshot
Developing a Crash Warning System for the Bike Lane Area at Intersections with Connected Vehicle Technology
inferredcites →https://doi.org/10.1177/1541931213571433openalex-snapshot
“Take over!” How long does it take to get the driver back into the loop?
inferredcites →https://doi.org/10.3141/2602-12openalex-snapshot
Steer or Brake?: Modeling Drivers’ Collision-Avoidance Behavior by Using Perceptual Cues
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