I am a big believer in rehearsing the future versus attempting to predict it. The wild swings we experience when following future scenarios can range from bold predictions of imminent manifestation to dire warnings that a scenario will never be realized. In this Recent Article, the author describes how the auto industry is rethinking the timetable to realizing level 5 autonomy. Turns out we underestimate the human intelligence required  to drive a car and overestimate our ability to replicate it. The article provides simple examples:

When a piece of cardboard blows across a roadway 200 yards ahead, for example, human drivers quickly determine whether they should run over it or veer around it. Not so for a machine. Is it a piece of metal? Is it heavy or light? Does a machine even “know” that a heavy chunk of metal doesn’t blow across the roadway? It’s a tougher problem.

Or how about this challenge that humans for the most part handle very well:

When a car arrives at a four-way stop at the same time as another vehicle, for example, it’s a dilemma for a machine. Human drivers tend to nod or make eye contact, but micro-controllers can’t do that.

When I speak of rehearsing, I am talking about possible futures. Rehearsing allows us to analyze the implications of scenarios and their potential paths. Figure 1 demonstrates an exercise focused on assessing the implications of the autonomous vehicle scenario. Rehearsing allows us to look at the breadth and depth of a scenario – and in most cases, the implications are broader and deeper than we realize.

The same need to rehearse is proving critical to the complex challenge of replicating human intelligence. In the case of level 5 autonomy, testing – either by driving physical miles or performing software simulations – is critical to realization. Since software simulations can’t foresee every eventuality, driving physical miles is required. As a result, most experts are estimating that the number of test miles will be measured in the billions. Toyota, for example, has publicly stated it needs 8.8 billion test miles for safe deployment of self-driving vehicles.

Many in the space believe it is unlike anything they’ve seen before. “It’s the most engineering-intensive thing ever attempted,” said one automotive executive in an off-the record discussion with Design News. “And you need lots of the world’s best engineers to do it. I’m not talking about tens or hundreds of engineers. It’s in the thousands. We’re talking about billions of dollars.” Replicating human intelligence relies on an understanding of the situations that the vehicle will encounter. For example, to deal with the four-way stop sign, Some vehicle developers are now teaching vehicles to inch forward while monitoring the other vehicle for implied consent.

This complexity makes our rehearsing difficult. As we understand the implications of a scenario, we then look for likely paths (Figure 2). Over time, obstacles (snow, cardboard, etc,) come into view – but so do possible accelerators. These obstacles and accelerators impact the path and timetable of a scenario, creating this dynamic of imminent realization or dire warnings. Confusion follows, as leaders try to understand the impact to their organizations.

While some now say we may never see level 5 autonomy, others like Tesla Inc. CEO Elon Musk has maintained his belief that his company will have full autonomy in 2020. “My guess as to when we think it’s safe for somebody to essentially fall asleep and wake up at the destination – probably towards the end of next year,” he said in a February podcast. More recently, he doubled down on that statement, saying he plans to have more than a million robo-taxis on the road in 2020.

In spite of the obstacles, the article underscores that virtually every automaker and supplier is forging ahead at full throttle. “It’s inevitable,” said Stewart Sellars, general manager of the LiDar Group for Analog Devices, Inc. “It’s going to happen. The only question is how long it will be before we can walk into a dealership and buy a Level 5 car.”

I explored this particular scenario in depth via several earlier posts:

• Autonomous Vehicles: An Interview with Chunka Mui
• Artificial Intelligence Intersects with Autonomous Vehicles
• Autonomous Vehicles: The Automotive Ecosystem
• Autonomous Vehicles: A Disruption Case Study
• The Growth of the Autonomous Car Market