Autonowashing is a concern for us all, because its consequences have the potential to effect us all.
Those who want to see driving automation advance & succeed—especially, and no matter what companies you root for—have an interest in speaking out against this issue.
Autonowashing is *not* limited to any one entity. This problem is rampant across the industry.
Tesla is discussed in relation to autonowashing, proportionately, as they continue to do the most obvious autonowashing of any OEM.
We can and should have respectful debate about what the remedies for autonowashing are. This is so important!
But pretending like “it’s not real!” or if it is that “it’s irrelevant!” is misguided and, in growing consensus, against our scientific knowledge.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
Plastics are a prob. Dead animals are often found w/ plastic waste in their stomaches. Plastics also breakdown into invisible micro-particles which we then might consume. Emerging research on the effects of microplastics on our health doesn't look good.
.@EuroNCAP has announced its new Assisted Driving Grading system which takes a holistic approach to sys evaluation by including "Driver Engagement" in its rating, to "help consumers" &to "compare assistance performance @ the highest level."
This is a win for human-automation interaction/HMI researchers who have been working for decades to explain how important teaming is and the consequences of broken control loops.
This is a win against #autonowashing, and ultimately a big win for consumer transparency & safety!
Further, @EuroNCAP also released the results of their 2020 Assisted Driving Tests with the new grading system and gave ten different ADAS systems a rating:
We have different ideas about how to “solve” for L5, and various teams are all taking shots at it. In recent years, two schools of thought have emerged about how to approach solving this problem.
🧵👇
For some it is either:
1) a fundamental AI problem which needs a new approach 2) a data problem, which can be solved by more data & more simulation
Some see the greatest challenge as developing the right AI approach.
Others believe that they already have the right approach, and therefore the challenge is acquiring more (and the right) data and doing more training.
Imo, there is some truth in both schools of thought.
As someone living through this pandemic who happens to study trust, human vigilance & behavior––it's entirely unsurprising that we're unable to maintain adequate COVID19 prevention measures. It's Psych 101 and it is why it's so important to have policies to help keep us in line.
It's the same with other safety-critical things (ex. vehicle automation); we need bounding boxes to keep us safe.
Human vigilance is like a muscle, but instead of getting stronger with use (+ as long as nothing bad happens) it weakens over time.
Therefore, what has been the most disturbing is watching the trust get knocked out of the policies we need to protect us, via their politicization and constant reversal and reimplementation.
This is an academic, @TheOfficialACM conference focused on interfaces and interactions in automotive applications. As you can imagine, a great focus is placed on vehicle automation