Motor vehicle artificial intelligence expert system dangerous driving warning and control system and method
Specifically programmed, integrated motor vehicle dangerous driving warning and control system and methods comprising at least one specialized communication computer machine including electronic artificial intelligence expert system decision making capability further comprising one or more motor vehicle electronic sensors for monitoring the motor vehicle and for monitoring activities of the driver and/or passengers including activities related to the use of cellular telephones and/or other wireless communication devices and further comprising electronic communications transceiver assemblies for communications with external sensor networks for monitoring dangerous driving situations, weather conditions, roadway conditions, pedestrian congestion and motor vehicle traffic congestion conditions to derive warning and/or control signals for warning the driver of dangerous driving situations and/or for controlling the motor vehicle driver use of a cellular telephone and/or other wireless communication devices.
1 . An artificial intelligence motor vehicle danger driving warning and control method comprising:
artificial intelligence decision making comprising an electronic, specifically programmed, communication computer system machine with artificial intelligence machine learning with expert system generation of motor vehicle danger driving and control signals;
derivation of motor vehicle driving condition parameters for said motor vehicle based on monitoring operational status of said motor vehicle parameters and further based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;
storing in memory said motor vehicle driving condition parameters and including parameters derived from monitoring the driver of said motor vehicle, drivers of said other motor vehicles, and/or said pedestrians;
storing in memory artificial intelligence machine learning algorithms to assist in setting monitored driving condition parameter threshold levels used in derivation of said motor vehicle danger driving and control signals based on evaluation of degree of danger values;
storing in memory database information recording motor vehicle driver driving habits, acumen, or ability to react to particularly dangerous situations, said database information based on artificial intelligence machine learning;
storing in memory expert defined propositional logic inference rules specifying multiple multidimensional conditional parameter relationships between two or more of said motor vehicle driving condition parameters including said motor vehicle driver's and pedestrian's parameters;
artificial intelligence expert system analysis with said electronic, specifically programmed, communication computer system of one or more of said multiple multidimensional conditional relationships, and wherein said multidimensional conditional relationships result in combined parameter degree of danger values that may be different than degrees of danger values for individual parameters; and
triggering generation of motor vehicle danger driving warning and control signals based on said artificial intelligence expert system analysis of said motor vehicle driving condition parameters.
2 . The method of claim 1 wherein said artificial intelligence multiple multidimensional conditional parameter relationship analyses comprise analysis of a combination of parameters derived from said (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, (4) communication network connections with roadside monitoring and control units, and (5) parameters derived from monitoring said motor vehicle driver, drivers of said other motor vehicles, and/or said pedestrians.
3 . The method of claim 1 wherein said artificial intelligence multiple multidimensional conditional parameter relationship analyses are designed to avoid the occurrence of accidents.
4 . The method of claim 1 wherein said artificial intelligence multiple multidimensional conditional parameter relationship analyses comprises roadway surface conditions, roadway construction projects, roadway width, roadway incline, roadway location, roadway signage, the presence or absence of roadway control signaling including stoplights or warning lights or warning signs, the number of traffic lanes, railroad crossings, crossroads, or roadway accident history or other similar variables that impact safety considerations with respect to the roadway being traveled.
5 . The method of claim 1 wherein said artificial intelligence multiple multidimensional conditional parameter relationship analyses comprise increased number of pedestrians being present at special events including concerts and sporting events, school zones, shopping districts, parks, business districts or other areas with increased number of people walking around or bicycling.
6 . The method of claim 1 wherein said artificial intelligence multiple multidimensional conditional parameter relationship analyses comprise parameters describing communications with application servers via the internet, Internet-of-Things, cellular connections, or cloud-based servers.
7 . The method of claim 2 wherein parameters derived from monitoring the driver of said motor vehicle, drivers of said other motor vehicles, and/or said pedestrians comprise parameters derived from monitoring driver and/or pedestrian use of electronic devices.
8 . The method of claim 7 wherein said electronic devices comprise cellular telephones.
9 . The method of claim 7 wherein said electronic devices comprise tablet computers.
10 . The method of claim 7 wherein said monitoring comprises monitoring a driver with a camera facing the driver.
11 . The method of claim 10 further comprises image analysis facial recognition.
12 . The method of claim 10 wherein said monitoring comprises monitoring a driver's eye movements.
13 . The method of claim 10 wherein said monitoring comprises monitoring to ascertain driver's attention to driving said vehicle.
14 . The method of claim 7 wherein monitoring one or more of said pedestrians comprises monitoring pedestrian traffic or congestion in areas being traveled by said motor vehicle.
15 . The method of claim 7 wherein parameters derived from monitoring the driver of said motor vehicle and drivers of said other motor vehicles comprise monitoring said drivers with directional microphone devices.
16 . The method of claim 2 wherein said monitoring the driver of said motor vehicle, drivers of said other motor vehicles, and/or said pedestrians comprises monitoring a driver's or pedestrian's medical condition with a medical device.
17 . The method of claim 16 wherein said medical condition further comprises heart rate, blood pressure, breathing parameters, asthma conditions, incapacitation and/or other critical driver medical condition parameters.
18 . The method of claim 17 wherein said medical device is a wearable device comprising medical sensor cuffs, patches, implants, wrist bracelets, ankle bracelets, eyeball activity and/or condition sensors or other medical sensor technology implementations.
19 . The method of claim 16 wherein said medical device comprises sensors for monitoring monitor driver sobriety including alcohol intoxication or indication of the use of drugs including marijuana, heroin and the like.
20 . The method of claim 2 wherein said artificial intelligence multiple multidimensional conditional parameter relationship analyses comprise fuzzy logic combinations of multiple of said parameter combinations.