Raw LLM Responses

After my experience as a human factors engineer with a major aircraft manufacturer, I can tell you the trusting automation to always do the correct thing is a very huge issue. How to warn the system operator when the automation can’t an appropriate response to an unusual or non-normal situation is also a difficult problem to solve. With aviation automation under such dubious situations, the response is to automatically disconnect the automation and warn the pilot that intervention by the pilot is required. A big difference between airplanes a cars and trucks though, is the timeliness required of the need for human intervention. Interventions can be required for aircraft automation system failures or faults and when encountering unexpected or unanticipated situations. The same is true for cars and trucks on the road. Quite obviously the choice of what sensors to use for detection and monitoring the nearby environment is essential. The dilemma involves replicating the senses that a human uses to determine how to safely drive the car or truck. By the way, sensors also must account for the differences between a car and truck as well. The need for increased levels of vigilance at night compared to during daylight operations is also a factor requiring compensation. For the same reason that highways allow driving safely at higher speeds compared to driving on city streets or in residential areas near homes and schools, for example, also is a consideration that must be factored into the automation algorithm. I was quite surprised that the Tesla automation learning algorithm cannot “learn” as intuitively, or as directly as a human driver can. That the Tesla algorithm cannot only be programmed to respond to previously encountered situations. That it can’t learn more directly from newly encountered situations. The false sense of security reaction by the driver that the automation can properly respond to previously unknown, or completely unique situations is very frightening. I have always been concerned about a system that relies totally on the belief that nothing is detected in the path ahead of the vehicle and that sudden unanticipated intrusions into that “safety zone” need to be reacted to immediately to avoid a collision. This ‘eyes in the back of your head’ extra sense must be somehow replicated in the automation algorithm. Whereas, in aviation, intrusions into the safety zone or protection zone, aka flight path, are prevented by requiring potential intruders to fly at different altitudes and by allowing space between aircraft flying in the same direction. This is a significantly more serious problem for aircraft that operate in a three dimensional environment rather than the two directional environment of the road or highway. The cars and trucks, one does not even worry about objects coming at you that or not also on the road. Analogous to trains operating on railroad tracks that are designated only for other trains, I have believed that the only way to guarantee safety is to properly warn the operator of approaching intersections where crossing or merging traffic may be encountered. And, to alert when intrusions have occurred. Such a “closed” environment where most obstacles can be prevented from encroaching into the trains’ safety zone is the safest way to operate. The very same operating environment must be assured for cars and trucks on the road as well.