AI Risk Radar For Self-Driving Cars

There is a lot in the news with self-driving cars.  Google has one. Apple is building one. Mercedes already has a self-driving transport.  Even the kid (George Hotz) who carrier unlocked the iPhone and Playstation built himself a self-driving car.

You read about LIDAR systems (Wikipedia: Lidar (also written LIDAR, LiDAR or LADAR) is a remote sensing technology that measures distance by illuminating a target with a laser and analyzing the reflected light. Although thought by some to be an acronym of Light Detection And Ranging, the term lidar was actually created as a portmanteau of "light" and "radar".) and camera systems with real time video analysis, etc.

Several makes and models already partially autonomous functions like parallel parking and lane departure systems that warn you when you leave the lane. Some cars will autonomously steer on a highway when cruise control is on.  However what is missing from the whole autonomous driving vehicle is some real brains behind driving.

When a human is driving a car -- especially a human who is a good driver and taught to drive defensively -- they always have a layer of abstraction that I like to call a risk radar turned on.

The risk radar while driving is like a an analysis and environment supervisory circuit that is not directly involved in actually driving the car, but processes meta-data about driving.

For example, a risk radar would look at the outdoor temperature and if it is freezing, it would devote some cycles to see if there is black ice on the road.

A risk radar notices that in a two or more lane highway, if you are are driving in another vehicle's blind spot, you either slow down or speed up.

A risk radar notices that you are in a high deer collision area on the highway, so some extra cycles are devoted to looking for the pair of reflective phosphorescent eyes on the side of the road.

If you had to brake heavily to avoid collision, a risk radar will model the circumstance that caused that extreme corrective action and will be comparing all environmental parameters, conditions and events to watch out for similar situations.  When one or more of those risk factors are present, it goes into high alert mode.

A risk radar can actually modify the way that a vehicle is driven.  Self-driving cars will have to have sensors like an accelerometers to detect yaw and pitch due to high winds, or hydro-planing on a wet road,  Risk radar would note these things.

Risk radar would count cars and notice if the traffic was heavy.  Risk radar will notice the speed of travel of other cars and make inferences about it.

Risk radar will have a range of characteristics.  Not only will it have a library of risks and the mitigation actions associated with them, but it will also have both supervised and unsupervised machine learning.  In machine learning, supervised learning is where the machine is given the correct result so it can self-adjust weights and thresholds to learn risks and risk mitigation actions. By the same token, unsupervised learning is where the machine infers a function from input data (through various algorithms such as clustering etc).

The biggest element of risk radar for self-driving cars, is that it must be time-domain aware.  Time-domain awareness means that it must know that certain events and actions follow the arrow of time and may or may not be caused by the preceding state. This is state of cognition with time awareness is a form of artificial consciousness (see the blog entry below), and it important in implementing a high level autonomous decision as to what algorithm will be used to drive the car.  For example, if the risk radar warrants it, the car would move into a cautious driving algorithm. If the road was icy, the car would under-steer to prevent skidding.  This would require coordination between acceleration and braking that would be different from ordinary driving.

The risk radar necessary for cars would be an evolving paradigm.  Cars driving in downtown New York would have different risk points than cars self-driving in Minnesota.  Having said that, if there were a standardized paradigm for the interchange of trained neural nets, a GPS waypoint would load the appropriate neural nets into car for the geographic area that it was driving in.

The risk radar for self-driving cars is a necessity before these autonomous vehicles are seen ubiquitously on all streets.  It really is a Brave New World, and it is within our grasp.