Automation Tech a Tougher Sell for Older Consumers
A new J.D. Power study finds that younger people are generally more trusting of the technologies that make self-driving vehicles possible.
by Staff
May 2, 2016
Photo of self-driving car courtesy of Volvo.
2 min to read
Photo of self-driving car courtesy of Volvo.
Trust in automation technology — a critical step for the future of self-driving vehicles — is much higher among younger consumers than their older counterparts, a new J.D. Power study concluded.
The study also found that consumers are most interested in a number of automotive technology features that make use of the underpinnings of fully automated vehicles, such as radar, sensors, light detection and ranging (LIDAR), and cameras. Features with high consumer interest include smart headlights, night vision, lane change assist, traffic jam assist, medical emergency stop, smart intersection, and predictive vehicle control.
Ad Loading...
But when it comes to making the leap to fully automated cars, trust in the technology is directly linked to the age of the consumer, according to the study.
For the purposes of the study, researchers divided consumers into five age groups: Pre-Boomers (born before 1946), Baby Boomers (born 1946-1964), Gen X (1965-1976), Gen Y (1977-1994), and Gen Z (1995-2000).
More than half of Gen Y (56%) and Gen Z (55%) vehicle owners said they trust self-driving technology, compared with 41% of Gen X, 23% of Baby Boomers, and 18% of Pre-Boomers.
Further, only 27% of Gen X, 18% of Gen Y, and 11% of Gen Z consumers said they “definitely would not” trust the technology, while 39% of Baby Boomers and 40% of Pre-Boomers said the same.
The one view all generations share is a concern for technology security, specifically surrounding privacy and the potential for systems to be hacked, hijacked, or to crash --either the vehicle or the system itself.
Ad Loading...
“The level of trust is directly linked to the level of interest in a new technology among automobile buyers,” said Kristin Kolodge, executive director of driver interaction and human machine interface (HMI) research at J.D. Power. “Acceptance can be increased with exposure over time and experience with automated technologies. But trust is fragile and can be broken if there is an excessive number of incidents with automated vehicles.”
Gen Y and Gen Z vehicle owners are twice as likely as Gen X members and five times as likely as Boomers and Pre-Boomers to show interest in certain alternative mobility types, such as mobility sharing/co-ownership, journey-based ownership, and mobility on demand, according to the study.
Furthermore, the study found that 59% of Gen Y vehicle owners said they are “definitely” or “probably” interested in fully automated vehicles, and 32% of them would pay $3,000 or more for the technology. Among the four alternative mobility types, interest levels are highest among all generations for unmanned mobility.
Fleet drivers face constant visual, cognitive, and environmental interruptions the moment they hit the road. From roadside chaos to mental fatigue and digital overload, today’s biggest driving risks often come from outside the vehicle itself.
FLASH Weather AI has launched a first-of-its-kind hail prediction model capable of forecasting hail size and arrival time at 1-kilometer resolution up to 55 minutes ahead, giving fleets and insurers critical time to prepare for severe storms.
As litigation risk rises, vehicles are increasingly targeted. This Coca-Cola bottler shares how it’s reducing exposure through driver training, technology, and a proactive risk management approach.
From identity management to third-party certifications, the right technology partner should make security easier to manage. Here are the three building blocks that fleet managers need to stay in control as connected systems scale.
Distracted driving remains one of the most persistent risks in fleet operations. New approaches focus on removing mobile device use entirely while adding real-time safety support.
As distraction risks evolve, fleets are turning to smarter, more connected technologies to better understand what’s happening behind the wheel. Part 2 explores how these tools are helping identify risky behaviors and improve visibility across operations.
Distracted driving is often measured by what we can see—phones in hand, eyes off the road. But what about the distractions we can’t? A recent incident raises a bigger question about awareness, attention, and why subtle risks so often go unnoticed.
