Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situati
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Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations Hiroaki Hayashi 1
&
Naoki Oka 1 & Mitsuhiro Kamezaki 2 & Shigeki Sugano 1
Received: 19 June 2020 / Revised: 19 September 2020 / Accepted: 2 October 2020 # The Author(s) 2020
Abstract In semi-autonomous vehicles (SAE level 3) that requires drivers to takeover (TO) the control in critical situations, a system needs to judge if the driver have enough situational awareness (SA) for manual driving. We previously developed a SA estimation system that only used driver’s glance data. For deeper understanding of driver’s SA, the system needs to evaluate the relevancy between driver’s glance and surrounding vehicle and obstacles. In this study, we thus developed a new SA estimation model considering driving-relevant objects and investigated the relationship between parameters. We performed TO experiments in a driving simulator to observe driver’s behavior in different position of surrounding vehicles and TO performance such as the smoothness of steering control. We adopted support vector machine to classify obtained dataset into safe and dangerous TO, and the result showed 83% accuracy in leave-one-out cross validation. We found that unscheduled TO led to maneuver error and glance behavior differed from individuals. Keywords Autonomous driving . Situational awareness . Cognitive behavior . Unscheduled takeover
1 Introduction Automated driving (AD) systems have the potential to achieve safer car-society and to enhance the quality of life of people. However, there are still many issues for achieving a fully AD system. SAE International defines six levels of automation, from level 0 (no automation) to level 5 (fully vehicle autonomy) [1]. In level 3 systems, drivers are not required to monitor the road environment and are allowed to engage in nondriving related tasks (NDRTs), such as reading books. A * Hiroaki Hayashi [email protected] Naoki Oka [email protected] Mitsuhiro Kamezaki [email protected] Shigeki Sugano [email protected] 1
Department of Modern Mechanical Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, Tokyo, Japan
2
Research Institute for Science and Engineering(RISE), Waseda University, 17 Kikui-cho, Shinjuku-ku, Tokyo, Japan
takeover request (TOR) will be issued when the AD system reached functional limits, and then the drivers are required to intervene the vehicle control (takeover: TO) and start manual driving (MD) [2]. TO would be dangerous especially in ‘unscheduled situations’, in which the AD vehicles encounters unplanned roadworks or sudden accidents that cannot be dealt with the AD system. Figure 1 shows an example of unscheduled TO, where an obstacle (a crashed car) suddenly appears. The time from when TOR is issued until the ego-vehicle collides with the obstacle is called the time to collision (TTC). After TOR is issued, the driver holds the steering wheel and places his/her foot on the pedals. This is called physical engagem
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