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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 24; p. 9993
• Main Authors: Araluce, Javier, Bergasa, Luis M, Ocaña, Manuel, López-Guillén, Elena, Gutiérrez-Moreno, Rodrigo, Arango, J Felipe
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19-12-2022; MDPI
• Subjects: Accidents, Traffic - prevention & control; Actuators; Attention; Automation; Automobile Driving; Automobiles; Autonomous vehicles; Awareness; CARLA simulator; Computers; Driver behavior; Driverless cars; driving behaviour study; Experiments; gaze focalization; Humans; non-driving-related tasks (NDRTs); Pedals; Reaction Time; Semantics; Simulation; Steering wheels; take-over quality; take-over time; Toolkits; Transportation equipment industry; Germany; driving behaviour study; driver situation awareness; CARLA simulator; gaze focalization; take-over time; non-driving-related tasks (NDRTs); take-over quality
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22249993

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.