The old school way is to add logic based on the strengths and weaknesses of each sensor type. My example is not specific to automotive sensors (I haven't worked in the automotive sector, but I do have now-outdated experience in obstacle detection and ranging, along with avoidance algorithms).
Sonar sensors are most accurate at medium ranges, but they are notorious for detecting ghost objects that do not really exist. Infrared range sensors are more reliable but are only accurate at very short range. So when a sonar sensor detects an object 8.4 meters away, you use the infrared sensor to double check. If the infrared sensor says there's an object 9 meters away in the same direction, you assume the object is real but is actually 8.4 meters away. If the infrared sensor says the nearest object in that direction is 20 meters away, you assume the sonar sensor made something up.
If you have enough types of sensors, you can also use a "majority rule". If two of 3 sensor types agree, you assume the 3rd is an anomaly. Lidar is excellent for this because it is accurate across a very large range, so it tends to overlap with most of your other sensors. This increases that odds that when there is a disagreement, one of the agreeing sensors will be capable of accurately measuring the distance to the object.
Thanks for this. This is what I come to HN for — to learn something outside of my field.
Do AI systems have the potential to weight or inform those transactions based on historical historical data then? The “experienced” aspect of learning all the things that turned out to be true or false in previous comparisons or data decision points would seem to be the obvious missing piece, but I have never really understood the specifics.
Statistics. "Sensors disagree" is the EXPECTED result when you get a reading from multiple sensors, and the whole point of sensor fusion is that, if the sensors have independent error models, that disagreement IMPROVES your output.
unregistereddev|2 years ago
Sonar sensors are most accurate at medium ranges, but they are notorious for detecting ghost objects that do not really exist. Infrared range sensors are more reliable but are only accurate at very short range. So when a sonar sensor detects an object 8.4 meters away, you use the infrared sensor to double check. If the infrared sensor says there's an object 9 meters away in the same direction, you assume the object is real but is actually 8.4 meters away. If the infrared sensor says the nearest object in that direction is 20 meters away, you assume the sonar sensor made something up.
If you have enough types of sensors, you can also use a "majority rule". If two of 3 sensor types agree, you assume the 3rd is an anomaly. Lidar is excellent for this because it is accurate across a very large range, so it tends to overlap with most of your other sensors. This increases that odds that when there is a disagreement, one of the agreeing sensors will be capable of accurately measuring the distance to the object.
happytiger|2 years ago
Do AI systems have the potential to weight or inform those transactions based on historical historical data then? The “experienced” aspect of learning all the things that turned out to be true or false in previous comparisons or data decision points would seem to be the obvious missing piece, but I have never really understood the specifics.
mrguyorama|2 years ago
bipson|2 years ago
unknown|2 years ago
[deleted]
batmansmk|2 years ago
reedf1|2 years ago
esafak|2 years ago
https://en.wikipedia.org/wiki/Robust_statistics
https://doi.org/10.3390/s21020438