Development of a Novel Intelligent Speed Adaptation System Based on Available Sight Distance

Hazoor, Abrar; Lioi, Alessandra; Bassani, Marco · 2021 · Crossref

DOI: 10.1177/03611981211008885

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the persistent issue of speed-related crashes, which often occur when drivers exceed safe speeds for given road conditions, particularly where available sight distance (ASD) is limited. Current speed management countermeasures, such as enforcement cameras and traffic calming, are often ineffective or limited to specific locations. Furthermore, existing Intelligent Speed Adaptation (ISA) systems rely on static posted speed limits or sign recognition, failing to account for real-time visibility constraints caused by permanent or temporary sight obstructions. The authors aim to develop a novel ISA system that dynamically adjusts speed recommendations based on real-time ASD, ensuring the vehicle’s stopping distance (SD) remains within the visible distance. The study employs a methodological approach using a fixed-base driving simulator at the Politecnico di Torino. The researchers developed a co-simulation framework linking SCANeR Studio® for virtual environment rendering and MATLAB Simulink® for algorithm processing. A virtual sensor with a 120°×60° field of view was mounted on the simulated vehicle to detect road markers and calculate real-time ASD. The system compares this ASD against the calculated SD, which accounts for vehicle speed, perception-reaction time, tire-road friction, and longitudinal grade. Three ISA variants were implemented: (1) informative, displaying visual icons; (2) warning, emitting auditory beeps; and (3) intervening, automatically limiting throttle pressure to prevent exceeding a calculated safe speed threshold. The system was validated on a two-lane road alignment with eleven curves of decreasing radii and sight obstructions, comparing sensor-derived ASD values against geometric calculations from AutoCAD®. The results demonstrate that the virtual sensor successfully estimated ASD with high precision, with absolute differences between sensor data and AutoCAD calculations generally under 1 meter. The Simulink model processed data at 100 Hz with a response time of less than 0.01 seconds, ensuring real-time performance. Testing confirmed that the informative and warning variants successfully prompted drivers to reduce speed when ASD fell below SD. The intervening variant effectively prevented the vehicle from exceeding the safe speed threshold by disconnecting the accelerator pedal, though the authors noted that the deceleration rate required improvement to fully reach the threshold speed during interventions. The system also accurately adjusted ASD calculations based on the vehicle’s lateral position within the lane. The significance of this work lies in its contribution to simultaneous vehicle/infrastructure design principles by integrating real-time road geometry and visibility into onboard safety systems. By moving beyond static speed limits, this novel ISA approach offers a more adaptive solution for managing speed in hazardous conditions, potentially reducing crash frequency and severity. The successful validation in a virtual environment paves the way for future real-world applications using onboard sensors like LiDAR. Future research will focus on assessing driver acceptance, workload, and the system’s impact on driving behavior in real traffic scenarios.

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• perceptual countermeasures