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http://hdl.handle.net/1942/38070
Title: | Analysis of an advanced driver-assistance system to improve safety of cyclists overtaking by driver characteristics | Authors: | MAURIELLO, Filomena BRIJS, Tom Montella, Alfonso Galante, Francesco BRIJS, Kris ROSS, Veerle |
Issue Date: | 2022 | Source: | 8th Road Safety & Simulation International Conference: Road safety and Digitalization, Athens (Greece), 8-10 June 2022 | Abstract: | As part of the European Horizon 2020 project i-DREAMS**, a medium-fidelity driving simulator study was conducted to evaluate safety effects of a new advanced driver-assistance system (ADAS) on cyclists overtaking by cars. The proposed ADAS was designed with a multimodal human-machine interface, auditory as well as visual information, using a multistage warning system, alerting drivers well in advance about the potential danger so that an imminent collision with the cyclist may be avoided due to attentive drivers. The warning strategy is based on a combination of lateral clearance and time-to-danger parameters. Forty-eight drivers drove the two-lane rural highway experimental route twice, without and with the ADAS, in three different car-to-cyclist overtaking scenarios. Statistical tests were performed to evaluate the effects of drivers’ gender and age on the effectiveness of the ADAS system. Overall, the study results show that the proposed in-vehicle driving assistance system had a significant effect in increasing the lateral clearance and in reducing the aerodynamics forces that can destabilize the cyclist overtaken by car. The ADAS system was more effective for female and older drivers. Finally, the implementation in real word of the proposed driving aid system will have positive impacts on cyclist safety and comfort, making the car-to-cyclist overtaking less dangerous. | Keywords: | ADAS;overtaking;multimodal warning;multilevel warning | Document URI: | http://hdl.handle.net/1942/38070 | Category: | C2 | Type: | Conference Material |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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RSS_2022 paper_98.pdf Restricted Access | Conference material | 704.26 kB | Adobe PDF | View/Open Request a copy |
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