Self-driving vehicles safety system

• US 10,235,877 B1
• Filed: 11/17/2018
• Issued: 03/19/2019
• Est. Priority Date: 12/27/2017
• Status: Expired due to Fees

• Current Assignee: Christ G. Ellis
• Original Assignee: Christ G. Ellis
• Inventors: Ellis, Christ G.
• Primary Examiner(s): Lu, Shirley
• Application Number:
  US16/194,313
• Time in Patent Office: 122 Days
• CPC Class Codes: B60W 30/14 Adaptive cruise control
• CPC Class Codes: G01S 13/34 using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
• CPC Class Codes: G01S 13/865 Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/91 for traffic control G01S13/93 takes precedence
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 17/88 Lidar systems specially adapted for specific applications
• CPC Class Codes: G05D 1/0055 with safety arrangements
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G06V 20/584 of vehicle lights or traffic lights
• CPC Class Codes: G08G 1/005 including pedestrian guidance indicator
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0145 for active traffic flow control
• CPC Class Codes: G08G 1/087 Override of traffic control, e.g. by signal transmitted by an emergency vehicle
• CPC Class Codes: G08G 1/095 Traffic lights
• CPC Class Codes: G08G 1/163 involving continuous checking
• CPC Class Codes: G08G 1/164 Centralised systems, e.g. external to vehicles
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: H04W 4/40 : for vehicles, e.g. vehicle-to-pedestrians [V2P]

Abstract

Self-driving vehicles safety system, comprising synthesized and coordinated components and entities, including vehicles, pedestrians, and traffic control light mechanisms, exchanging information, employing lidar (light imaging detection and ranging), radar and intelligent computer-based decision support algorithm systems that analyze images and extract information, to provide safety and vehicle control, regulated and prioritized traffic, and reduced vehicle emissions.

Claims

1. A self-driving vehicles safety system comprising synthesized and coordinated entities that are functionally distinct from each other and that work in combination, wherein the self-driving vehicles safety system includes a lidar system, a continuous-wave radar system, a computer vision system, and an intelligent video monitoring and video analysis system, wherein the self-driving vehicles safety system includes components that include vehicle components, pedestrian components, traffic control light components, traffic control light mechanism components, and traffic complex components, and wherein the self-driving vehicles safety system includes entities that include other radar and sensor-based system entities, GPS entities, and cell phone technology entities, wherein the intelligent video monitoring and video analysis system comprises:

a) a system portion that is disposed in a vehicle as an integral part of the vehicle and that includes built-in or installable computer vision devices that provide high-level understanding of digital images and videos;

b) a system portion that develops a theoretical and algorithmic basis to achieve automatic visual understanding, that incorporates intelligent video monitoring and video analysis, that evaluates images, and that executes actions based on the data collected;

c) a system portion that enables vehicles to recognize other vehicles, evaluate imminent danger when sensed and execute actions based on the data collected, that stops and shuts down involved vehicles, and that takes control of threatening vehicles;

d) a system portion that enables vehicles to recognize pedestrians, evaluate imminent danger when sensed and execute actions based on the data collected, that stops and shuts down involved vehicles, and that takes control of threatening vehicles;

e) a system portion that enables vehicles to recognize traffic control lights, evaluate imminent danger when sensed and execute actions based on the data collected, that stops and shuts down involved vehicles, and that takes control of threatening vehicles;

f) a system portion that enables traffic control light mechanisms to recognize vehicles, evaluate imminent danger when sensed and execute actions based on the data collected, that stops and shuts down involved vehicles, and that takes control of threatening vehicles;

g) a system portion that enables traffic control light mechanisms to employ artificial intelligence, rendering instantaneous, protective action when undefined, unplanned danger is detected;

h) a system portion where autonomous video surveillance systems monitor vehicle traffic, evaluate imminent danger when sensed and execute actions based on the data collected, that stops and shuts down involved vehicles, and that takes control of threatening vehicles;

i) a system portion that detects a presence, direction, distance, and speed of a vehicle by sending out high-frequency waves that are reflected off said vehicle;

j) a system portion that monitors prioritized vehicles, that compares alert signals received and priority algorithms with predetermined standards, and that gives a go-ahead to prioritized vehicles accordingly;

k) a system portion that employs coordinated components and entities, that observes prioritized vehicle codes, that automatically provides information on optimum routing, closed and obstructed roadways and railroad crossings, that preempts traffic control lights, and that warns pedestrians of advancing prioritized vehicles;

l) a system portion responsive to electronic tags, to vehicles, and roadside objects, that collects target mass information to allow reliable pre-collision restraint deployment decisions;

m) a system portion where computer vision is the science of machines, robots, computer systems, video surveillance and artificial intelligence, employed to scan the surroundings, read, understand, and obey roadway lines, markings, symbols and signs, and to guide a vehicles behavior based on the data collected;

n) a system portion where video content analysis (VCA) is a technology used to analyze video for specific data, behavior, objects and attitude;

o) a system portion where autonomous video surveillance (IVS) systems recognize traffic control lights and evaluate the images and execute actions based on the data collected;

p) a system portion where computer vision technologies embed into autonomous video surveillance systems to monitor vehicle traffic, evaluate imminent danger sensed and execute actions based on the data collected, that stops and shuts down involved vehicles, and that takes control of threatening vehicles;

q) a system portion where computer vision technologies embed into video devices such as cameras, encoders, routers, digital video recorders (DVRS), network video recorders, and other video management and storage devices;

r) a system portion where images received by vehicles and other traffic mechanisms from different systems, including lidar system, a continuous-wave radar system, a computer vision system, and an intelligent video system, reinforce safety in detection and response;

s) a system portion where, by communicating with other vehicles, self-driving vehicles can receive information about upcoming obstacles, traffic congestion, and pedestrians on the road before they are in front of the vehicle or appear on a map;

t) a system portion employing computer vision deep learning algorithms that makes vehicles teach themselves how to evaluate images and execute actions based on the data collected;

u) a system portion where computer vision technologies embed in autonomous video surveillance, obtain a description of what is happening in a vital military domain and execute appropriate action based on that interpretation;

v) a system portion where computer vision technologies embed in autonomous video surveillance of noncooperative and camouflaged targets in cluttered outdoor settings and within a military domain, employ motion analysis, behavior analysis, and standoff biometrics for identification of known suspects, anomaly detection, and behavior understanding;

w) a system portion where computer vision technologies embed in autonomous video surveillance and employ systems for tracking and movement analysis to detect and identify abnormal and alarming situations associated with vehicle traffic;

x) a system portion where computer vision technologies embed in an autonomous video surveillance system, execute automatic abnormal motion detection and initiate video transmission and recording, triggering appropriate alarms, functions and vehicle behavior;

y) a system portion where computer vision technologies embed in an autonomous video surveillance system, detect an anomalous motion pattern caused by an individual merging into a crowd, including into a school, and trigger appropriate procedures and alarms; and

z) a system portion where autonomous video surveillance, employing video content analysis algorithms to automate repetitive tasks, enables the notification of a larger number of events in a shorter time.