Srinath Sibi

The RRADS platform: a real road autonomous driving simulator

This platform paper introduces a methodology for simulating an autonomous vehicle on open public roads. The paper outlines the technology and protocol needed for running these simulations, and describes an instance where the Real Road Autonomous Driving Simulator (RRADS) was used to evaluate 3 prototypes in a between-participant study design. 35 participants were interviewed at length before and after entering the RRADS. Although our study did not use overt deception---the consent form clearly states that a licensed driver is operating the vehicle---the protocol was designed to support suspension of disbelief. Several participants who did not read the consent form clearly strongly believed that they were interacting with a fully autonomous vehicle. The RRADS platform provides a lens onto the attitudes and concerns that people in real-world autonomous vehicles might have, and also points to ways that a protocol deliberately using misdirection can gain ecologically valid reactions from study participants.

Monitoring driver cognitive load using functional near infrared spectroscopy in partially autonomous cars

In partially automated cars, it is vital to understand the driver state, especially the drivers cognitive load. This might indicate whether the driver is alert or distracted, and if the car can safely transfer control of driving. In order to better understand the relationship between cognitive load and the driver performance in a partially autonomous vehicle, functional near infrared spectroscopy (fNIRS) measures were employed to study the activation of the prefrontal cortex of drivers in a simulated environment. We studied a total of 14 participants while they drove a partially autonomous car and performed common secondary tasks. We observed that when participants were asked to monitor the driving of an autonomous car they had low cognitive load compared to when the same participants were asked to perform a secondary reading or video watching task on a brought in device. This observation was in line with the increased drowsy behavior observed during intervals of autonomous system monitoring in previous studies. Results demonstrate that fNIRS signals from prefrontal cortex indicate additional cognitive load during manual driving compared to autonomous. Such brain function metrics could be used with minimally intrusive and low cost sensors to enable real-time assessment of driver state in future autonomous vehicles to improve safety and efficacy of transfer of control.

RRADS: Real Road Autonomous Driving Simulation

This video introduces a methodology for simulating an autonomous vehicle on open public roads. The video showcases participant reaction footage collected in the RRADS (Real Road Autonomous Driving Simulator). Although our study using this simulator did not use overt deception--the consent form clearly states that a licensed driver is operating the vehicle--the protocol was designed to support suspension of disbelief. Several participants who did not read the consent form clearly strongly believed that the vehicle was autonomous; this provides a lens onto the attitudes and concerns that people in real-world autonomous vehicles might have, and also points to ways that a protocol that deliberately used misdirection could gain ecologically valid reactions from study participants.

Marionette: Enabling On-Road Wizard-of-Oz Autonomous Driving Studies

There is a growing need to study the interactions between drivers and their increasingly autonomous vehicles. This paper describes a method of using a low-cost, portable, and versatile driver interaction system in commercial passenger vehicles to enable on-road partial and fully autonomous driving interaction studies. By conducting on-road Wizard-of-Oz studies in naturalistic settings, we can explore a range of driving conditions and scenarios far beyond what can be conducted in laboratory simulator environments. The Marionette system uses off-the-shelf components to create bidirectional communication between the driving controls of a Wizard-of-Oz vehicle operator and a driving study participant. It signals to the study participant what the car is doing and enables researchers to study participant intervention in driving activity. Marionette is designed to be easily replicated for researchers studying partially autonomous driving interaction. This paper describes the design and evaluation of this system.

Effect of cognitive load in autonomous vehicles on driver performance during transfer of control

Present study on cognitive workload in driving focuses on reduction of workload for better driving performance. In this paper, we talk about the cognitive load in drivers of autonomous cars and their performance under multiple cognitive loads. Our previous studies have indicated that low to no workload is likely to induce drowsiness in drivers of autonomous vehicles. We hypothesize that there is an optimal cognitive load for a driver during autonomous driving for best performance after transfer of control from autonomous to manual. We propose an experiment to study the cognitive load on the driver of a simulated autonomous car and the effects on manual driving performance. We also describe our use of biometric devices to obtain physiological measures indicative of cognitive workload.

Embodied Design Improvisation for Autonomous Vehicles

We have developed a generative, improvisational and experimental approach to the design of expressive everyday objects, such as mechanical ottomans, emotive dresser drawers and roving trash barrels. We have found that the embodied design improvisation methodology—which includes storyboarding, improvisation, video prototyping, Wizard-of-Oz lab studies and field experiments—has also been effective in designing the behaviors and interfaces of another kind of robot: the autonomous vehicle. This chapter describes our application of this design approach in developing and deploying three studies of autonomous vehicle interfaces and behaviors. The first, WoZ, focuses on the conceptual phase of the design process, using a talk-aloud protocol, improvisation with experts, and rapid prototyping to develop an interface that drivers can trust and hold in esteem. The second, the Real Road Autonomous Driving Simulator, explores people’s naturalistic reactions to prototypes, through an autonomous driving interface that communicates impending action through haptic precues. The third, Ghost Driver, follows the public deployment of a prototype built upon frugal materials and stagecraft, in a field study of how pedestrians negotiate intersections with autonomous vehicles where no driver is visible. Each study suggests design principles to guide further development.