Driver Take-Over Behaviour Study Based on Gaze Focalization and Vehicle Data in CARLA Simulator

Author:

Araluce JavierORCID,Bergasa Luis M.ORCID,Ocaña ManuelORCID,López-Guillén ElenaORCID,Gutiérrez-Moreno Rodrigo,Arango J. Felipe

Abstract

Autonomous vehicles are the near future of the automobile industry. However, until they reach Level 5, humans and cars will share this intermediate future. Therefore, studying the transition between autonomous and manual modes is a fascinating topic. Automated vehicles may still need to occasionally hand the control to drivers due to technology limitations and legal requirements. This paper presents a study of driver behaviour in the transition between autonomous and manual modes using a CARLA simulator. To our knowledge, this is the first take-over study with transitions conducted on this simulator. For this purpose, we obtain driver gaze focalization and fuse it with the road’s semantic segmentation to track to where and when the user is paying attention, besides the actuators’ reaction-time measurements provided in the literature. To track gaze focalization in a non-intrusive and inexpensive way, we use a method based on a camera developed in previous works. We devised it with the OpenFace 2.0 toolkit and a NARMAX calibration method. It transforms the face parameters extracted by the toolkit into the point where the user is looking on the simulator scene. The study was carried out by different users using our simulator, which is composed of three screens, a steering wheel and pedals. We distributed this proposal in two different computer systems due to the computational cost of the simulator based on the CARLA simulator. The robot operating system (ROS) framework is in charge of the communication of both systems to provide portability and flexibility to the proposal. Results of the transition analysis are provided using state-of-the-art metrics and a novel driver situation-awareness metric for 20 users in two different scenarios.

Funder

Spanish MICINN/FEDER through the Artificial Intelligence based modular Architecture Implementation and Validation for Autonomous Driving (AIVATAR) project

RoboCity2030-DIH-CM project

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference44 articles.

1. Jimenez, F. (2017). Intelligent Vehicles: Enabling Technologies and Future Developments, Butterworth-Heinemann.

2. A taxonomy of autonomous vehicle handover situations;McCall;Transp. Res. Part A Policy Pract.,2019

3. A Dual-Cameras-Based Driver Gaze Mapping System with an Application on Non-Driving Activities Monitoring;Yang;IEEE Trans. Intell. Transp. Syst.,2019

4. Santos, G.H.G., and Larocca, A.P.C. (2019, January 10–14). Drivers Take-Over Performance from Partial Automation to Manual Driving. Proceedings of the 2019 33th Congresso de Pesquisa e Ensino em Transporte da ANPET, Balneário Camboriú, Brasil.

5. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017). CARLA: An open urban driving simulator. arXiv.

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