Master Thesis Worker
Göteborg, SE, 417 56
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Master thesis proposal
How AEB systems make urban buses safer for passengers and vulnerable road users?
Problem Description
Buses are an essential means of urban transportation and are generally considered safe. However, crashes involving buses can cause severe injuries and fatalities to vulnerable road users (VRUs) such as pedestrians and cyclists. In order to protect VRUs, Advanced Driver Assistance Systems (ADAS) are being developed and introduced to the market. One of these ADAS, is an Automatic Emergency Braking (AEB) system that uses forward-facing sensors to detect potential collisions [1]. If a collision is imminent and the driver does not react, the system automatically applies the brakes to prevent or reduce the severity of the crash.
While effective in many scenarios, we do not, however, know the occurrence of false positives (FP) [2]. FPs are events where the system triggers unnecessary braking in response to, for example, harmless objects, ambiguous environmental cues, or newly emerging micromobility vehicles. Such false activations not only degrade drivers’ trust and passenger comfort but can also lead to passenger injuries. Current safety performance assessments of these systems lack the inclusion of false positives especially in complex driving conditions. Accurate assessment is important to quantify the expected real-world benefit of these systems for passenger and VRU protection.
Objectives
This thesis aims to develop and evaluate methods for detecting false positives in AEB activation using real-world driving data that includes vehicle kinematic signals (speed, acceleration, yaw rate), and video recordings from forward- and side-facing cameras. The objectives are:
• to propose a methodology for using ground truth data to detect FP that includes
o finding automatically flagged events where AEB is triggered
o reviewing, labeling, and classifying the events from the field data into FP
o quantifying FP braking rate
• to analyze patterns in FP AEB activations by correlating kinematic features, environment context and infrastructure
• to validate the proposed methods on a representative dataset of real-world driving scenarios, with quantitative evaluation of both safety (collision avoidance) and operational performance (false activation reduction).
Prerequisites: Python or Matlab.
Number of students: 1 ‐ 2.
Information and contact:
Volvo: Zain Zubair, (zain.zubair@consultant.volvo.com) (financial compensation from Volvo will be awarded to the students)
Chalmers: Jordanka Kovaceva (jordanka.kovaceva@chalmers.se)
References:
[1] Knight et al., (2022), The Transport for London Bus Safety Standard: Advanced Emergency Braking (AEB).
[2] Wikipedia contributors, Confusion matrix, https://en.wikipedia.org/wiki/Confusion matrix, 2025, [Online].
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