Background: As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. This paper aims to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. Methods: We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. Results: There was a significant difference in the tasks (F(2,33) = 4.34, p = 0.0213), identifying the game-task as significant with respect to reaction time and ideal for the present investigation. Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. Conclusion: The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments with AR-Games focusing on the real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.
In recent years, surface Electromyography (sEMG) signals have been effectively applied in various fields such as control interfaces, prosthetics, and rehabilitation. We propose a neck rotation estimation from EMG and apply the signal estimate as a game control interface that can be used by people with disabilities or patients with functional impairment of the upper limb. This paper utilizes an equation estimation and a machine learning model to translate the signals into corresponding neck rotations. For testing, we designed two custom-made game scenes, a dynamic 1D object interception and a 2D maze scenery, in Unity 3D to be controlled by sEMG signal in real-time. Twenty-two (22) test subjects (mean age 27.95, std 13.24) participated in the experiment to verify the usability of the interface. From object interception, subjects reported stable control inferred from intercepted objects more than 73% accurately. In a 2D maze, a comparison of male and female subjects reported a completion time of 98.84 s. ± 50.2 and 112.75 s. ± 44.2, respectively, without a significant difference in the mean of the one-way ANOVA (p = 0.519). The results confirmed the usefulness of neck sEMG of sternocleidomastoid (SCM) as a control interface with little or no calibration required. Control models using equations indicate intuitive direction and speed control, while machine learning schemes offer a more stable directional control. Control interfaces can be applied in several areas that involve neck activities, e.g., robot control and rehabilitation, as well as game interfaces, to enable entertainment for people with disabilities.
Autonomous driving system (ADS) is anticipated to revolutionize travel by reclaiming lost time and improve safety on the roads. With automation, user-engagements that enhances road monitoring should be considered to maintain vigilance and safety. From the literature, virtual reality (VR) usage in cars offer productivity and increased privacy. This paper explores the efficacy of passenger use of VR headsets to enhance user-engagement during transit. User-engagement was quantified using physiological measures (pupillary response and electrodermal activity) during an in-car VR game/activity experiment. Further, the impacts of engaging with secondary tasks was evaluated using reaction time of pop-up objects. We designed a driving simulation with inbuilt entertaining activities, no-task, game-task, video-task, and mixed-task, played in a real car with a FOVE VR headset on the perimeter track of the Gifu University campus with 15 subjects (average 25.6 years, SD = 6.4).From reaction time, significant difference between tasks was found using one-way ANOVA (F(3,231) = 2.75, p = .0437). A post-hoc test revealed that game and mixed task reaction times were significantly different (p = .0126 and p = .016, respectively) suggesting that task design should consider hazard recognition in a real car. From physiological measures, an increased/sustained effect of user engagement was noted compared with baseline (no-task) suggesting effectiveness in maintaining vigilance. The results also reported a 10-fold improvement in sitting posture compared to baseline. The methodology employed is applicable as an indirect measure of engagement that would find use in productivity and vigilance study in an ADS.
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