Supporting Drivers in Keeping Safe Speed in Adverse Weather Conditions by Mitigating the Risk Level

Gallen, Romain; Hautière, Nicolas; Cord, A.; Glaser, Sébastien · 2013 · OpenAlex-citations

DOI: 10.1109/tits.2013.2262523

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the critical issue of overspeeding as a primary cause and aggravating factor in traffic accidents, particularly on secondary roads where static speed limits fail to account for dynamic hazards like adverse weather. The authors propose a novel method for an adaptive Intelligent Speed Adaptation (ISA) system that computes a safe speed profile by mitigating risk levels rather than relying solely on stopping distance criteria. The approach is motivated by the need to maintain consistent safety margins regardless of environmental conditions, such as reduced friction or visibility, which significantly increase crash severity and probability. The methodology defines "highway risk" as the combination of accident probability and potential severity, modeled through a generic emergency braking scenario where a driver reacts to an obstacle. The system utilizes a top-down approach, modulating a reference speed—defined as the 85th percentile of observed speeds ($V_{85}$) under ideal conditions—to ensure the risk level in adverse conditions matches that of ideal conditions. This is achieved through an "Equivalent Total Risk" (ETR) criterion. The model incorporates detailed vehicle dynamics, including longitudinal and lateral acceleration constraints, friction coefficients, road geometry (curvature, slope, superelevation), and driver parameters like reaction time and brake pedal pressure. Crash severity is assessed using delta-V metrics fitted to injury probability statistics for slight, serious, and fatal outcomes. The system was validated using actual data collected from a French secondary road, as well as tests on a test track and a fleet of vehicles. The experiments demonstrated that the proposed method effectively adjusts speed recommendations based on real-time environmental inputs, such as rain intensity affecting friction. Results indicated that the system could accurately compute braking speed profiles that account for the trade-off between longitudinal braking and lateral trajectory keeping in curves. The tests showed that reducing speed according to the ETR criterion successfully mitigated the increased risk associated with wet roads and poor visibility, maintaining the same statistical probability of injury as in dry conditions. The significance of this work lies in its potential to enhance road safety by providing drivers with adaptive, context-aware speed recommendations that go beyond static legal limits. By focusing on risk mitigation rather than just stopping distance, the system offers a less constraining yet safer alternative to existing ISA strategies. The study concludes that the proposed method is generic and adaptable to various vehicles and road networks, showing great interest for deployment in cooperative intelligent transportation systems. This approach addresses the stagnation in safety benefits from traditional speed enforcement by dynamically adapting to the specific hazards of secondary roads and adverse weather conditions.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-25; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• speed choice