This accounts for my ongoing skepticism of fully autonomous unpiloted automobiles in unpredictable highway conditions. Speaking as someone with over a million miles of professional driving and no accidents in the era before any automation, I'm a fan of computer assisted driving, but mystified by the emphasis on achieving the last 10% (or so) required to entirely replace a human driver. Most freight handling and most auto passenger service jobs require a significant component of presence from a human--traditionally involving the human vehicle driver.** They might as well handle that crucial last 10%. Occasionally on the great highway, drivers and navigators encounter anomalous phenomena, presenting the sort of problems for which a computer lacks proper context, and could never learn on the fly.
Anyway, my suspicion about the techies working so intensively and expensively on making a fully remotely programmed vehicle is that they never did the job. Or, anyway, enough of the job to really have a grasp of it. Speaking of what Greg Wagner mentioned in that LinkedIn note quoted in the post.
I sometimes indulge a suspicion that the people most invested in the AUV project think it's of utmost importance because they hate to drive: they're aggravated and frustrated by the experience, view humans as accident-prone (although we're also accident preventable, with some forethought), and don't drive very well themselves, especially in traffic. So they're committed to replacing all that messy human quasi-parallel processing with the One True Ideal Algorithm to rule it all.
[ *more difficult to acquire with an auto navigating urban-suburban-rural streets than with OTR trucking. ]
[**theoretically, robots. Thereby inducing an extra level of Fragility. Robot get sick, no two aspirin work.]
Absolutely and a fantastic observation. In my work on AVs, I realized quickly that trying to replace a human, as a human drives is hyper complex. But if you could add systems on the roads, that can communicate to the AVs, you could significantly reduce the complexity. Think intersections that manage queueing, road surfaces that communicate lanes and what's coming up, etc.
You're undoubtedly more well-apprised of the technical details than myself. But my intuition is that it would increase the amount of centralized surveillance, toward the direction of the Panopticon. with a central command grid, as streamlined as might be when working optimally--when it breaks, it all breaks.
Although I suppose a decentralized network of sensors operating on the basis of temporary proximity could work too, for some purposes. Although, maintenance problems. Local maintenance problems, worse some places than others. Rural maintenance problems.
Eventually, things happen on the road (or near it) that are just plain weird: a wedge of plyboard or a length of 2"x4" comes hurtling at the windshield, and evasive action is possible with a glance in the mirror to confirm lane clearance. Or a 2-wheel cargo trailer detaches from the bumper of the car ahead and to the right on the freeway, and it just keeps rolling, bearing left. Or sudden dust storms (a worse condition than blizzards, although those are also daunting.) Someone puts up a gate on a road that might have previously been open, possibly with those dragon teeth. Or there might just be a wire across. Problems with steep slopes and low gear, on unpaved roads--or icy driveways. All problems that have happened to me. Or all of the other various problems related to stuff that just doesn't belong happening, but happens anyway. And a human can put together the big picture right away, while the machine goes tilt. How does the machine know when to stop, when a truly novel combination of circumstances arises?
The surveliance network is certainly an issue to consider. There's also another twist here: We forgive humans for mistakes. We don't have forgiveness for technology which is something I've written about here:
Yes, I've considered the question of allowing a reasonable amount of tolerance for intelligent machine mistakes. Ultimately, the question can only be assessed empirically, with all driving conditions and their challenges accounted for. I don't know what the latest figures on total miles driven by these vehicles have reached, but I think the significance has to be compared with the Vehicle Miles Traveled by human piloted vehicles: an estimated 3,190,000,000,000 miles per year.
And the Manhattan Project ignored by all data scientists and managers: develop a means and effective metric[s] that measures the quality and veracity of all data going into and coming out of the systems. Not a matching relationship check but a genuine means to measure the truth, provenance and "meaningfulness" of data and aggregated pools of it.
This accounts for my ongoing skepticism of fully autonomous unpiloted automobiles in unpredictable highway conditions. Speaking as someone with over a million miles of professional driving and no accidents in the era before any automation, I'm a fan of computer assisted driving, but mystified by the emphasis on achieving the last 10% (or so) required to entirely replace a human driver. Most freight handling and most auto passenger service jobs require a significant component of presence from a human--traditionally involving the human vehicle driver.** They might as well handle that crucial last 10%. Occasionally on the great highway, drivers and navigators encounter anomalous phenomena, presenting the sort of problems for which a computer lacks proper context, and could never learn on the fly.
Anyway, my suspicion about the techies working so intensively and expensively on making a fully remotely programmed vehicle is that they never did the job. Or, anyway, enough of the job to really have a grasp of it. Speaking of what Greg Wagner mentioned in that LinkedIn note quoted in the post.
I sometimes indulge a suspicion that the people most invested in the AUV project think it's of utmost importance because they hate to drive: they're aggravated and frustrated by the experience, view humans as accident-prone (although we're also accident preventable, with some forethought), and don't drive very well themselves, especially in traffic. So they're committed to replacing all that messy human quasi-parallel processing with the One True Ideal Algorithm to rule it all.
[ *more difficult to acquire with an auto navigating urban-suburban-rural streets than with OTR trucking. ]
[**theoretically, robots. Thereby inducing an extra level of Fragility. Robot get sick, no two aspirin work.]
Great post I just read, on the relationship between complexity and fragility: https://karlastarr.substack.com/p/welcome-to-peak-complexity-why-modern
Absolutely and a fantastic observation. In my work on AVs, I realized quickly that trying to replace a human, as a human drives is hyper complex. But if you could add systems on the roads, that can communicate to the AVs, you could significantly reduce the complexity. Think intersections that manage queueing, road surfaces that communicate lanes and what's coming up, etc.
You're undoubtedly more well-apprised of the technical details than myself. But my intuition is that it would increase the amount of centralized surveillance, toward the direction of the Panopticon. with a central command grid, as streamlined as might be when working optimally--when it breaks, it all breaks.
Although I suppose a decentralized network of sensors operating on the basis of temporary proximity could work too, for some purposes. Although, maintenance problems. Local maintenance problems, worse some places than others. Rural maintenance problems.
Eventually, things happen on the road (or near it) that are just plain weird: a wedge of plyboard or a length of 2"x4" comes hurtling at the windshield, and evasive action is possible with a glance in the mirror to confirm lane clearance. Or a 2-wheel cargo trailer detaches from the bumper of the car ahead and to the right on the freeway, and it just keeps rolling, bearing left. Or sudden dust storms (a worse condition than blizzards, although those are also daunting.) Someone puts up a gate on a road that might have previously been open, possibly with those dragon teeth. Or there might just be a wire across. Problems with steep slopes and low gear, on unpaved roads--or icy driveways. All problems that have happened to me. Or all of the other various problems related to stuff that just doesn't belong happening, but happens anyway. And a human can put together the big picture right away, while the machine goes tilt. How does the machine know when to stop, when a truly novel combination of circumstances arises?
The surveliance network is certainly an issue to consider. There's also another twist here: We forgive humans for mistakes. We don't have forgiveness for technology which is something I've written about here:
https://www.polymathicbeing.com/p/zero-forgivness-for-technology
Yes, I've considered the question of allowing a reasonable amount of tolerance for intelligent machine mistakes. Ultimately, the question can only be assessed empirically, with all driving conditions and their challenges accounted for. I don't know what the latest figures on total miles driven by these vehicles have reached, but I think the significance has to be compared with the Vehicle Miles Traveled by human piloted vehicles: an estimated 3,190,000,000,000 miles per year.
Waymo unpiloted taxicabs operating in four US cities drove a total of 25,300,000 miles in 2024. https://www.nbcbayarea.com/investigations/waymo-driverless-cars-safety-study/3740522/. Expressed as a ratio, that's around 0.000008 of all VMT in 2023.
And the Manhattan Project ignored by all data scientists and managers: develop a means and effective metric[s] that measures the quality and veracity of all data going into and coming out of the systems. Not a matching relationship check but a genuine means to measure the truth, provenance and "meaningfulness" of data and aggregated pools of it.
That's a very interesting insight.