I'll take a small chance of being accidentally killed as a systemic, correctable mistake over the small chance of being killed by the 5% of people who are consistently impatient, inconsiderate dangerous jerks.
I'll take a small chance of being accidentally killed as a systemic, correctable mistake over the small chance of being killed by the 5% of people who are consistently impatient, inconsiderate dangerous jerks.
I think the most dangerous time to be a cyclist or pedestrian is right now. Smartphones are ubiquitous, distracted-driving enforcement is very weak (non-existent in my area), and self-driving is alpha quality.
SF is a completely different experience for bikes than it was 5 years ago, and 10 years before that it was even more different.
Ditto for NYC. You can ride up and down Manhattan in big separate big lanes very quickly.
It's obviously a personal choice, but I think cycling is a safe choice now. When I started 10+ years ago I would sometimes get off the road when conditions/traffic looked dangerous, and you should always keep that option in your back pocket. (i.e. don't insist that 100% of the route has to be bikable.)
But I don't feel the need to do that very much now. The exception is when there's unusual traffic and no bike lane.
It's even safer now that there are more cyclists on the road. Drivers do take some time to get used to cyclists.
But it isn't perfect - SF fwiw has had more pedestrian fatalities this year though they've had reductions in the prior couple years ( https://www.visionzerosf.org/wp-content/uploads/2019/07/06.2... )
Also average vehicle miles travelled in the US is up ( https://fred.stlouisfed.org/series/M12MTVUSM227NFWA/ ), and so are nationwide pedestrian fatalities: https://usa.streetsblog.org/2019/01/23/the-bible-belt-should...
Ride-hailing usage over transit may be part of the problem: https://steps.ucdavis.edu/new-research-ride-hailing-impacts-... ) .
In general, AI algorithms are famously opaque and hard to debug.
It's too long to explain here, but here are some links:
https://en.wikipedia.org/wiki/Explainable_artificial_intelli...
https://www.wired.com/story/greedy-brittle-opaque-and-shallo...
https://blogs.wsj.com/cio/2018/05/02/companies-grapple-with-...
https://thenextweb.com/artificial-intelligence/2017/04/23/ar...
https://towardsdatascience.com/need-for-explainability-in-ai...
https://www.forbes.com/sites/cognitiveworld/2018/12/20/geoff...
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BTW I'm also a cyclist. Human drivers can be annoying, but I find that they do act predictably. I've been cycling 5+ days a week in SF for 10+ years. I find that the drivers are worse when I bike in cities where bikes aren't as prevalent. Humans do learn. When you get into a conflict, there are emotions, and emotions are mechanisms for learning :)
It seems like you think that an AI is "just" a computer program, where the errors ARE indeed systematic and correctable. But AI is fundamentally different.
1. The classification shouldn't happen from a single frame. In order to be misclassified as a shadow, the system would have to get the classification incorrect at various distances and you're probably moving too, so it would need to be across many frames.
2. This suggests that one sensor misclassifying will be sufficient for a deadly situation. But autonomous vehicles all use multiple sensors. They could even theoretically have multiple classification models running. One misclassification will not result in accidents.
3. Humans driving around effectively blindly is already an extremely dangerous situation.
4. This discussion is often limited to places where humans are distracted but overall trying to drive properly. In many parts of Asia (Korea, Taiwan, etc.) drivers very willingly disobey signals and are not at all deterred by the risk of hitting a pedestrian through their own impatience.
Humans are very dangerous behind the wheel. Hypothesizing about corner cases where autonomy might make a mistake is missing the forest for the trees.
You can have road bikes, mountain bikes with similar sitting positions, or recumbents, which will need a very different training profile, or tandems, etc
Bikes are severely underrepresented in the list of things that car automation companies care about at the moment, so any self-driving car on the road today might misidentify bikes.
Bikes also often ride in positions (from the perspective of the car) that can be unexpected. Some can be as close to the side of the car that it can cover the full field of view of a side-looking camera for example. How do self-driving algorithms work when encountering such cyclists?
I sure don't want to find out myself - I would hope car companies are doing tests on closed courses with cyclist mocks or inputs.
How can you possibly know that?
So, why fall back on the status quo of a human to spot them? If SDCs are proven to do it better, then I will definitely cycle more once there are more SDCs. It's all about evidence, show me the evidence SDC's are better, then I'll be 95% on board.