Self-driving Car Crashes: Safety Lessons for Businesses Racing into the Future

Self-driving Car Crashes: Safety Lessons for Businesses Racing into the Future

by
Sara R Moulton

At the Singapore Business Leaders Programme (SBLP) this week, we heard about global megatrends shaping our world and Asia, disruptions and innovations, and Industry 4.0. Building on such topics, this article takes the example of self-driving cars to derive lessons for organisations, especially those going full speed into automation. Who or what do we keep safe?

Over the last few years, there have been a handful of crashes involving self-driving cars. In more recent memories, on March 23, 2018, a Tesla in autopilot mode crashed into a highway divider in California. Just a few days earlier on March 18, a pedestrian was crossing the road when an Uber self-driving car going 65 kilometres per hour hit her. There is an ongoing investigation as to why the car did not stop for the victim, Elaine Herzberg.

The first fatality in an autonomous vehicle occurred in 2016. The driver, Joshua Brown, died when the Tesla car he was in hit a truck that was turing. Tesla explained that this accident was due to a technical failure and later released information that Brown ignored warnings to keep his hands on the wheel.

In each of these tragedies, there is ambiguity about whether it was the car’s fault or the human’s fault. While we may never get closure on this, these accidents suggest lessons that businesses should consider, even as they are accelerating towards the next big breakthrough.

Take the example of autonomous driving

There are six levels of autonomous driving. They are:

Level 1: Driver assistance required. While the human driver is still in control of the car, there is some assistance like with steering or accelerating.

Level 2: Partial automation available. Either steering and/or acceleration or deceleration is autonomously controlled. The driver needs to be ready to take control, if need be.

Level 3: Conditional automation. Human drivers can allow the car to take over “safety-critical functions”. The driver needs to be ready to take control, but is not required to monitor second-by-second like in other levels.

Level 4: High automation. Level 4 vehicles are designed to perform all safety-critical driving functions and monitor roadway conditions for an entire trip.” However, this does not cover every single driving scenario such as poor weather or unusual environments.

Level 5: Full automation. The car is fully autonomous where the car is expected to act like a human driver completely.

Today’s autonomous driving cars sit at a Level 3. Will there be more casualties as they rev up towards Level 5?

Lessons from these crashes

Thinking more deeply, here are three lessons in ‘safety’ for businesses pioneering breakthroughs and innovations:

Businesses need to introduce change at a palatable pace. It might be that Uber and Tesla rushed to roll out the technology too soon, which led to disaster and setbacks. We will never know for sure, but this reminds us of the importance of not rushing all the time.

Pushing too fast too soon can lead to ‘disaster’.

For both Tesla and Uber, they have faced backlash, including criticism for not taking blame, investigation from the National Transportation Safety Board, and a crisis of confidence from consumers. Perhaps it is worthwhile to have moved more slowly in the first place, in order to get buy-in from the ground up from all quarters. This brings us to the next lesson.

2) Figure out what ‘safety’ really means to stakeholders in times of change.

As a company innovates, leaders need to identify stakeholders’ needs. In this case, stakeholders include all important points of contact. Be sure to communicate openly and quickly and ensure that emotional safety needs are met. For different stakeholders – the board, employees, shareholders, and the community – each may need a different type of frequency in communication.

As an aside, many jobs and industries will change as automation and artificial intelligence take over objective tasks. According to McKinsey research, few occupations are fully automatable, but 60% of all occupations have at least 30% technically automatable activities.

An example of a job that could, one day, be automated is accounting. For many accountants, they deal with collecting and processing data, and tasks which are sometimes repetitive. Now, accountants may dislike their mundane jobs, but at least they have employment today. What happens to these accountants and their livelihoods when machine learning becomes smart enough to handle all accounting for individuals, or even small companies?

What are the implications if their emotional safety needs are not met?

3) Scan the world for markets who are ready to leave their safety nets and be early adopters.

The US is pushing for autonomous driving but is not getting buy-in as much as countries like China. In China, people believe in the technology and therefore it is likely China will overtake the US in autonomous driving. Why is that? “China is poised to lead in self-driving cars–and it’s not because of technology,” author Karen Hao says, “they trust the technology and feel more secure about passing on their data than Americans.”

Meanwhile, South Korea has allocated US$33.1 billion for smart highways, which will service electric vehicles and driverless cars.

Hence, for businesses pioneering the next big thing, do not launch it indiscriminately in just any market. Ask yourself where the early adopters for your company’s products or services are. Test bed your products and services in these markets where resistance to change is less of a mountain to climb. Upon seeing safety, the more risk-averse markets will gradually follow!

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