Beyond the First Self-Driving Ride
Using behavioral science to accelerate consumer adoption in automated mobility
As you read this, a fleet of autonomous vehicles are driving themselves around the streets of Boston to learn the roads, whilst people in Las Vegas are using an app to hail driverless taxis in Las Vegas.
But how does that make you feel? What would it take for you to be comfortable riding in a self-driving car? How would you react seeing the wheel turn without human hands, navigating through urban traffic.
Don’t be alarmed if these questions make you feel uncomfortable or anxious. As humans we are wired to be fearful of change, we’ve acted that way for centuries and it isn’t going to change any time soon. But to really move forwards and accelerate consumer adoption of new technologies, particularly automated mobility, there are steps that can be taken to help minimize that fear. It’s an area I’ve been studying and learning for years now at Aptiv — observing human reactions, emotions, key behavior shifts, likes and dislikes when they interact with autonomous vehicles. Here’s a behind the scenes look at some of our learnings.
A System in Transition
All work in automated mobility is transitional. It starts with creating a safe environment, giving consumers first-hand experiences with autonomous vehicles, measuring responses and adjusting systems. As new advances in automation technology occur (e.g., expanding operational domains, better sensor resolutions), they also cause consumers to update their mental models of how an automated vehicle works and how accurately it perceives situational risks.
That’s why it is so essential to adopt a phased transition-friendly mindset in the product philosophy of any work in this space. This means consumers can be taken through the various stages of on boarding and allowed to build trust over time. Research has taught me that at roughly 20+ automated rides, passengers begin to internally adjust and transition their human-robot trust. They gain experiential knowledge of an automated vehicle’s capabilities, limitations, behaviors, and driving style “personality.”
Future automated ride sharing passenger’s experiences are often compared to that of a commercial airline passenger. It’s true that we’ll all eventually become seasoned riders (much like in aviation), but it’s going to take a calibrated approach to get there. We are not seasoned riders in vehicle autonomy just yet.
Calibrating Trust
The establishment, maintenance and in certain situations, the regaining of trust revolve in a continuous loop. Ground-truth reliability and actual performance of an autonomous vehicle might only be a few of the many characteristics affecting a person’s trust and notion of safety.
Research shows that transparency in decision-making and perceived understanding of an autonomous maneuver plays an important part in the overall calibration of trust. For example, the use of forewarnings helps humans adjust their trust accordingly.
In one of our on-road sessions, participants experienced a set of scripted autonomous vehicle limitations (e.g., sudden hard braking in the middle of a turning maneuver). Participants who were not given any indication or prior warnings of upcoming situational anomalies reported a loss in trust (an average three points decrease on a scale of one to 10).
They also perceived a high level of environmental risk or system malfunction for each scenario. Participants who were made aware of possible upcoming system limitations and/or situational anomalies calibrated their trust accordingly and reported lesser deviations in trust (an average one point decrease on a scale of one to 10).
Key trust-building interactions also consider the cognitive and affective experiences of a passenger, especially when the dynamics of automation are involved. While situational awareness is a well-researched field for manual driving, in a shared mobility ecosystem it is important to take a closer look at another phenomenon called “passenger situational awareness.”
While not responsible for actual latitudinal and longitudinal control of a vehicle, passengers take on certain supervisory functions in a ride, subconsciously maintaining an internal orientation and a high-level status of their journey. Put another way, passengers regularly look outside their windows to orient themselves in an environmental context.
Our on-road sessions repeatedly show that passengers heavily rely on vehicle speed variations and the visceral experience of vehicle motion to intrinsically assess certain driving contexts and states that might need their attention.
Research Techniques
There are two main challenges when performing any kind of consumer research in the field of emerging technologies. First, it is extremely hard for participants to simply “imagine” the future and their expected behaviors around such systems. It is comparable to asking someone who has never seen an iPhone about how would they use such a device.
Second, what participants “say” about their beliefs and expectations can be drastically different than what they actually “do” or “feel.” We have to actively frame participants in a future context in which we chip away at any superficial responses.
For example, certain initial views on autonomous systems can be bifurcated: either it’s a perfect system or an unreliable system. In later sessions, however, participants are often more eager to develop a human-robot team relationship and certain system behaviors no longer erode trust.
Here are some methods in action:
· Co-creation exercises: In these types of scenarios participants are asked to design a given solution and actively participate in the process. While such exercises might seem fun and playful they often offer an excellent reflection into the consumer’s mind and how that person frames a given problem.
· Simulation with a twist: Once participants are actively placed in a future context, simulation is used to run through a set of repeated controlled road scenarios. Driving simulators are used for driver-focused research and training all around the world. Simulated sessions are additionally run, focussing on the passenger and collecting data on situational awareness, trust, line-of-sight, and a range of other passenger behaviors in an automated context.
· On-road sessions: While a simulator setup allows for road scenario controllability and eases data collection, on-road automated sessions are required to really give dynamic road context and assess passenger reactions.
Technology Adoption
It is highly likely that before reaching 100 percent market share, autonomous vehicles and shared mobility will follow Rogers bell-shaped adoption curve. Here, the initial customer segment of innovators and early adopters are characterized as risk-takers, technology-focused, and experimental in their adoption approach.
Leveraging the initial building blocks of trust and having an understanding of consumer behaviors within this segment will undoubtedly accelerate our understanding of future segments of majority adopters. It will not only help to speed up adoption, but also improve the design, functionality and user experience of mobility as we know it today. And for me, that’s precisely where behavioral science gets really exciting — when it is used to shape the future and make it a less daunting place for everybody.
