Analysis of Volatility in Driving Regimes Extracted from Basic Safety Messages Transmitted Between Connected Vehicles
Asad Khattak, Behram Wali

TL;DR
This paper analyzes driving volatility by identifying distinct driving regimes from connected vehicle safety messages, using Markov switching models to improve hazard detection and safety systems.
Contribution
It introduces a novel application of Markov switching models to classify driving regimes from BSM data, enhancing understanding of driving behavior and volatility.
Findings
Acceleration and deceleration are distinct regimes.
Deceleration is more volatile than acceleration.
Three regimes: high-rate acceleration, high-rate deceleration, and cruise.
Abstract
Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
