A Driver Situational Awareness Estimation System Based on Standard Glance Model for Unscheduled Takeover Situations
| Autor: | Takaaki Ema, Lollett Catherine, Udara E. Manawadu, Takahiro Kawano, Shigeki Sugano, Hiroaki Hayashi, Tomoya Tomita, Mitsuhiro Kamezaki |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
Situation awareness Computer science 05 social sciences Control (management) Driving simulator Cognition Convolutional neural network Task (project management) Action (philosophy) Order (exchange) Human–computer interaction 0502 economics and business 0501 psychology and cognitive sciences 050107 human factors |
| Zdroj: | 2019 IEEE Intelligent Vehicles Symposium (IV). |
| DOI: | 10.1109/ivs.2019.8814067 |
| Popis: | Highly-automated vehicles operating in level 3 issue a takeover request (TOR) to transfer the control authority from the autonomated driving (AD) system to a human driver when they encounter system limitations. In such ‘unscheduled’ situations, the driver is required to immediately re-engage in the driving task both physically and cognitively, and perform suitable action, e.g. lane change. Thus, evaluating driver engagement by the AD system would lead to safe takeover. Physical engagement is easily estimated but there are few studies on evaluating cognitive engagement. In this study, we thus developed a driver situational awareness estimation system based on glance information. We first defined seven standard glance areas and driver glance classification model using a convolutional neural network. We then obtained a large amount of glance data when both safe and dangerous takeover situations (lane change) by using a driving simulator, and we derived the standard glance model including the glance area and time, in order to estimate whether driver gained enough cognitive re-engagement in real-time. To evaluate the effectiveness of the proposed model, we created a situational awareness assist system to visually indicate regions with insufficient glance. As a result, we found that the assist system drastically improved driving performance and reduced the number of accidents during takeover. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|