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Better safe than sorry

Some of us find it hard to trust a machine to take control of something as important as driving. We might worry that the AI could make a mistake or fail, resulting in catastrophe. It’s still a relatively new technology, and some of us might be uncomfortable with the idea of not having a human driver at the wheel.

Moreover, self-driving cars could be vulnerable to hacking or other cybersecurity threats, which could compromise the safety of the passengers or lead to other consequences. And what about all that data being recorded by cars driving around? Who controls the use of that information? As self-driving vehicles become more prevalent, there will be new questions and challenges related to cyber security, data, personal integrity, liability, insurance, regulations, etc. Many of us might only embrace AD once these issues are resolved.

And honestly, many of us genuinely enjoy the act of driving and the sense of control and freedom it provides. So why would we give up this pleasure and autonomy to a machine?

And we won’t, not completely. Cars will still have steering wheels. Currently, full automation is more of a robotaxi thing. For passenger cars, it’s probably more about developing a nice, smooth relationship with automation. Anyway, any hesitation to give up driving might also stem from concerns regarding safety, trust, and the potential societal impact of this technology. However, as self-driving technology continues to develop and prove its safety and efficiency, it’s likely that skeptics will gradually become comfortable with the idea – just like it took years, decades, of safety improvements for us to accept riding in elevators or getting into airplanes. The shift from exclusive indulgence for the few to secure widespread adoption usually requires time.

What we can do to convince the public that automation is safe is to explain the various criteria our solutions will meet.

For one, there’s the legal aspect. This is when AD systems demonstrate their safety by showing they comply with standards, certifications, regulations. And this is important; without standardization, we cannot ensure a given product’s quality, safety, and inter-compatibility. Signing such a societal contract also makes AD development move forward on a unified front, gradually instilling more confidence in the general public. Some will argue that this is enough. Design a system that complies with the rules and you’re good.

However, this standards-based approach has its flaws. Even if you fulfill the criteria set by a standard, can you really be sure your exceedingly complex end goal is met? What’s your level of ambition? That’s something you might have to take a lot of responsibility for yourself.

Put differently: a compliance-based approach would be enough in a perfect world where everyone did as they were told. As they were trained to do. To be sure, a strictly “rule-based” approach makes it easier (at least hypothetically) to determine whose fault is in a crash. However, an “It wasn’t me!” approach won’t prevent the accident from happening. It’s pointless in the bigger picture, where any accident should be avoided, not just the ones we would be blamed for.

We’ve already touched on the fact that people often break traffic rules. They speed, get distracted, misinterpret signs, run through intersections, and so on. Generally, we’d view these actions as improper, yet ironically, they can sometimes help the traffic system function. For instance, wouldn’t we consider nudging past a red light by a meter or two, if it meant making way for an ambulance, provided it was safe to do so?”

In a sense, then, the game is rigged: how successful can a risk-averse robot following robot rules really be in a world of “flexible” humans?

We’re convinced that developing technology that “follows the rules” and considers human behavior is the safest bet. It shouldn’t matter whose fault the accident is. And it won’t if we can avoid the accident altogether.

It’s a little bit like this: If you crash into a tree, what’s the lesson learned? Learn how to not drive into trees (and similar objects)? Remove all trees close to the road? Plan to drive only where you can clearly see the road? We suggest doing all of the above, leaving you prepared in case there’s a tree left behind that suddenly decides to fall over the road.

The key takeaway? You need a comprehensive approach to automation, one that incorporates both proactive and reactive measures. The central concept behind our approach to safety is to drive cautiously, enabling collision avoidance and permitting emergency maneuvers in the event of unforeseen occurrences.

At some point, however, we must also show that our solutions work. Following good old evidence-based practice, we might share collision avoidance statistics and the number of hours driven without accidents or severe injuries. We can demonstrate how we’ve reduced the number of emergency interventions by drivers using our software. This will give an indication of how well the software performs. But it’s not enough. Such data is not available until years after the technology is introduced and what good is it then for assuring someone that the technology is safe from day one?

Even if we could come up with great statistics and an impressive track record for automation, it’s still “so far, so good thinking. It’s generalizing based on observations and examples. It’s inductive. After all, launching new software is like taking a new drug to market: you hope it will take care of the problem it’s designed to eliminate without causing unpleasant side effects.

If AD systems are meant to reduce human errors in traffic, they shouldn’t come with their own bag of issues, right?

The journey to widely accepted automated driving is a challenging but necessary path. By focusing on a comprehensive approach to safety that balances compliance with standards, understanding human behavior, and capacity for dealing with the unexpected, we are well on our way to convincing the public of the reliability and benefits of this revolutionary technology. By providing evidence-based results and predictive safety assurance, we are paving the way towards a future where automated driving is not just accepted, but trusted and preferred.

Through patience, transparency, and continuous improvements, we believe that this transformative technology can redefine automotive safety and automation.

Stay tuned.

19 April 2023


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