A Grid-based Surrogate Safety Measure for Traffic Safety Assessment

Research output: Contribution to journalConference articlepeer-review

Abstract

Assessing the traffic safety and an impact of Advanced Driver Assistance Systems (ADAS) on it based on collision data is a challenging task since the crashes are rare and usually there is a lack of high quality accident data. Still, in order to be able to evaluate the performance of ADAS, different Surrogate Safety Measures (SSMs) are continuously introduced in the literature. Unfortunately, single SSMs have in general a limited application scope so that combining several SSMs seems a sensible option. However, combining them to produce a single value is difficult, as there is not a clear guideline about the different weights. Against this background, in this paper we propose a grid-based SSM characterizing the traffic safety by 6 categories ranging from conflict-free to safety critical. The advantages of the approach include the ability to perform a more comprehensive traffic safety analysis or an efficient comparison of different ADAS by their impact on safety. The latter one is illustrated by simulation-based comparison of three Adaptive Cruise Control (ACC) set-ups.
Original languageEnglish
Number of pages6
JournalInternational Conference on Connected Vehicle and Expo (ICCVE)
DOIs
Publication statusPublished - 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Fields of science

  • 206002 Electro-medical engineering
  • 207109 Pollutant emission
  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202027 Mechatronics
  • 202034 Control engineering
  • 203027 Internal combustion engines
  • 206001 Biomedical engineering

JKU Focus areas

  • Digital Transformation
  • Sustainable Development: Responsible Technologies and Management

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