Autonomous driving has been one of the most interesting technologies in recent years with expectation of solving accidents, pollution, and traffic jams (Jo, Lee, & Kim, 2013; Schrank, Eisele, & Lomax, 2012; Singh, 2018). However, current autonomous vehicles cannot handle all driving situations. Therefore, drivers must intervene in certain situations. SAE international defined these levels of autonomous driving as partial (level 2) and conditional (level 3) autonomous driving (SAE international, 2016). In level 3 autonomous driving, drivers are not required to monitor the driving situations and they may perform non-driving related tasks (NDRTs). However, drivers must pay attention to driving situations and make an appropriate reaction when takeover request (TOR) occurs. Takeover request (TOR) is one of the major issues in autonomous driving. A human driver must be ready to transfer the control of the vehicle when TOR is given. Therefore, how and when to request the driver to transfer the control is important. In this regard, takeover lead time (TORlt) has received great attention and there are many existing scholarly works on the effect of TORlt on takeover performance (Gold et al., 2013; Gold et al., 2017; Mok, Johns, Lee, Ive et al., 2015; Mok Johns Lee, Miller et al., 2015; Payre et al., 2016; Van den Beukel & Van der Voort, 2013; Wan & Wu, 2018; Zhang et al., 2018). Besides its impact on takeover performance, TORlt also has an effect on driver workload (Eriksson & Stanton, 2017; Wan & Wu, 2018). Inappropriate TORlt makes driver overload or underload and the abnormal workload deteriorates driver performance in takeover situation (De Winter et al., 2014; Eriksson & Stanton, 2017; Hajek et al., 2013; Wan & Wu, 2018). However, these studies either did not investigate workload induced by TOR or measure driver workload in a subjective method. This study focused on workload induced by TOR. Wan & Wu (2018) stated that takeover request without sufficient time budget may increase driver workload and generate erratic driver's response. However, many researches have focused on workload while performing NDRT alone. Additionally, a few research that assessed workload induced by TOR used subjective methods. The objective of this study is to investigate the effects of TORlt on driver workload in takeover situation. This study hypothesizes that workload would increase when TORlt is too short or too long. To investigate the hypothesis, an experiment was conducted with driving simulator and workload was measured by subjective and objective methods. The experiment with driving simulator was conducted with 28 participants to investigate the effect of TORlt on the driver workload. TORlt was controlled in 7 levels (3s, 7s, 10s, 15s, 30s, 45s, 60s). Each session of the experiment was dealt with one TORlt level and was conducted in random sequence. At the beginning of the session, participants had to perform the NDRT during autonomous driving. Then, they are required to identify an obstacle in ego-lane and make a lane change to avoid the collision when TOR occurs. The dependent variables in this experiment include workload measured by subjective and physiological methods. Driving Activity Load Index (DALI; Pauzie, 2008) was conducted to measure subjective workload and physiological measures including respiration rate (RSP), heart rate (HR), and galvanic skin reponse (GSR) were conducted to evaluate objective workload. The results of this study showed that TORlt had a significant effect on subjective workload. Subjective workload was increased in short TORlt as expected. Drivers, who were given the TOR with short lead time, did not have sufficient time to perceive and comprehend the driving situation nor make an appropriate decision. As a result, drivers could not cope with the takeover situation and their workload increased. However, driver workload was not increased in excessively long TORlt. Long TORlt was expected to increase driver workload because driver could assume long TORlt to be a false alarm or feel distraction (Wan & Wu, 2018). This might be because participants did not consider that 60s of TORlt was long or there was no false alarm in the experiment. There was no significant effect of TORlt on mean RSP and mean HR. This is because each participant behaved differently or regarded driving situation after the takeover as a simple driving task. In contrast to RSP and HR, TORlt had a significant effect on mean GSR. According to Kramer (1991), Physiological signals are sensitive to different resource demands (Kramer, 1991; Ryu & Myung, 2005). In this study, excessive temporal demand because of short TORlt and distraction caused by long TORlt were demands imposed on the participants. Hence, GSR which is sensitive to emotion and frustration (Kramer, 1991) was influenced by TORlt. In conclusion, the results of the study were different from the hypothesis which expected excessive workload with too short or long TORlt. Even though subjective workload and GSR partially support the hypothesis, more complicated and controlled experiment is needed to confirm the hypothesis. In the future research, experiment including complex driving task and false alarm should be conducted and it is necessary to measure physiological signals while controlling various resource demand to investigate the effect of TORlt on physiological signals.
Multimodal Cue Combinations: A Possible Approach to Designing In-Vehicle Takeover Requests for Semi-autonomous Driving