AAC 2022 Paper Abstract

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Paper TuAT4.7

Leoni, Jessica (Politecnico di Milano), Tanelli, Mara (Politecnico di Milano), Strada, Silvia (Politecnico di Milano), Savaresi, Sergio (Politecnico di Milano)

Assessing E-Scooters Safety and Drivability: A Quantitative Analysis

Scheduled for presentation during the Regular Session "Vehicle Dynamics and Control" (TuAT4), Tuesday, August 30, 2022, 11:40−12:00, Ballroom

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on April 25, 2024

Keywords AI/ML application to automotive and transportation systems, Intelligent transportation systems, AI/ML and model based approaches for safety and security in automotive systems

Abstract

E-scooters are now massively present in urban environments as a shared last-mile solution. Their apparent ease of drivability favored their diffusion, but also actually raises safety concerns. In this work, we propose a data-driven approach to map e-scooters mechanical specifications to drivability and safety metrics, the latter appropriately defined and computed based on experimental data. An ad-hoc HW/SW platform was designed for this purpose, so as to be portable and installed on seven different e-Scooters that were tested in trips carried out in the city of Milan. The proposed approach allowed us to characterize both stability and comfort metrics on the different vehicles, and compare them also with a qualitative driving test carried out by a journalist making driving tests. The quantitative analysis matched the impressions of the human driver, and it disclosed an interesting mapping between the perceived risk and the vehicle characteristics evaluated according to the two metrics proposed.

 

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