DE-102021101320-B4 - Method and system for determining the presence of a hidden danger

DE102021101320B4DE 102021101320 B4DE102021101320 B4DE 102021101320B4DE-102021101320-B4

Abstract

Method (1000) for determining the presence of a hidden danger, via a control system, comprising: Identifying an operating scene (200, 400, 600, 700, 800, 900) for a host vehicle (101, 201, 401, 601, 701, 801, 901) based on information that includes information corresponding to the current geographic location of the host vehicle (101, 201, 401, 601, 701, 801, 901); Identifying an operating situation for the host vehicle (101, 201, 401, 601, 701, 801, 901) based on information that includes information corresponding to dynamic conditions within the operating scene (200, 400, 600, 700, 800, 900); Collecting and classifying information from a variety of proximity sensors (119), wherein the proximity sensors (119) are connected to the host vehicle (101, 201, 401, 601, 701, 801, 901) and include an ambient light sensor, a vision system, an acoustic sensor and include a seismic sensor; Estimating a multitude of hidden hazard presence probabilities according to the information from each of the multitude of proximity sensors (119), the operating scene (200, 400, 600, 700, 800, 900), the operating situation and a comparison process (1013); and performing a fusion process with the multitude of hidden hazard presence probabilities to determine the presence of a hidden hazard; where the comparison process (1013): a) retrieving rules corresponding to the operating scene (200, 400, 600, 700, 800, 900) and the operating situation, defining overlap and non-overlap zones within a headlight pattern of the host vehicle (101, 201, 401, 601, 701, 801, 901), creating brightness comparisons for the zones, and comparing the brightness comparisons with collected and classified areas of overlapping light within the headlight pattern of the host vehicle (101, 201, 401, 601, 701, 801, 901); or b) retrieving acoustic signatures corresponding to the operating scene (200, 400, 600, 700, 800, 900) and the operating situation, and comparing the signatures with collected and classified acoustic waveforms; or c) includes retrieving seismic signatures that correspond to the operating scene (200, 400, 600, 700, 800, 900) and the operating situation, and comparing the signatures with collected and classified seismic waveforms.

Inventors

Prakash Mohan Peranandam
Erik B. Golm
Meng Jiang
Shengbing Jiang
Jiyu Zhang

Assignees

GM Global Technology Operations LLC

Dates

Publication Date: 20260513
Application Date: 20210122
Priority Date: 20200214

Claims (2)

Method (1000) for determining the presence of a hidden hazard, via a control, comprising: identifying an operating scene (200, 400, 600, 700, 800, 900) for a host vehicle (101, 201, 401, 601, 701, 801, 901) based on information which includes information corresponding to the current geographic location of the host vehicle (101, 201, 401, 601, 701, 801, 901); Identifying an operating situation for the host vehicle (101, 201, 401, 601, 701, 801, 901) based on information that includes information corresponding to dynamic conditions within the operating scene (200, 400, 600, 700, 800, 900); collecting and classifying information from a variety of proximity sensors (119), wherein the proximity sensors (119) are connected to the host vehicle (101, 201, 401, 601, 701, 801, 901) and include an ambient light sensor, a vision system, an acoustic sensor, and a seismic sensor; Estimating a multitude of hidden hazard presence probabilities according to the information from each of the multitude of proximity sensors (119), the operating scene (200, 400, 600, 700, 800, 900), the operating situation and a comparison process (1013); and performing a fusion process with the multitude of hidden hazard presence probabilities to determine the presence of a hidden hazard; wherein the comparison process (1013): a) retrieving rules corresponding to the operating scene (200, 400, 600, 700, 800, 900) and the operating situation, defining overlap and non-overlap zones within a headlight pattern of the host vehicle (101, 201, 401, 601, 701, 801, 901), creating brightness comparisons for the zones, and comparing which includes brightness comparisons with collected and classified areas of overlapping light within the headlight pattern of the host vehicle (101, 201, 401, 601, 701, 801, 901); or b) retrieving acoustic signatures corresponding to the operating scene (200, 400, 600, 700, 800, 900) and the operating situation, and comparing the signatures with collected and classified acoustic waveforms; or c) retrieving seismic signatures corresponding to the operating scene (200, 400, 600, 700, 800, 900) and the operating situation, and comparing the signatures with collected and classified seismic waveforms.
System (100) for determining the presence of a hidden hazard, comprising: a host vehicle (101, 201, 401, 601, 701, 801, 901); a plurality of proximity sensors (119) connected to the host vehicle (101, 201, 401, 601, 701, 801, 901) comprising an ambient light sensor, a vision system, an acoustic sensor, and a seismic sensor; A controller configured to: identify an operating scene (200, 400, 600, 700, 800, 900) for the host vehicle (101, 201, 401, 601, 701, 801, 901) based on information that includes data corresponding to the current geographic location of the host vehicle (101, 201, 401, 601, 701, 801, 901); identify an operating situation for the host vehicle (101, 201, 401, 601, 701, 801, 901) based on information that includes data corresponding to dynamic conditions within the operating scene (200, 400, 600, 700, 800, 900); The system collects and classifies information from the multitude of proximity sensors (119); estimates a multitude of hidden hazard presence probabilities based on the information from each of the multitude of proximity sensors (119), the operating scene (200, 400, 600, 700, 800, 900), the operating situation, and a comparison process (1013); and performs a fusion process with the multitude of hidden hazard presence probabilities to determine the presence of a hidden hazard; wherein the comparison process (1013) includes: a) retrieving rules corresponding to the operating scene (200, 400, 600, 700, 800, 900) and the operating situation, defining overlap and non-overlap zones within a headlight pattern of the host vehicle (101, 201, 401, 601, 701, 801, 901), creating brightness comparisons for the zones, and comparing the brightness comparisons with collected and classified areas of overlapping light within the headlight pattern of the host vehicle (101, 201, 401, 601, 701, 801, 901); or b) retrieving acoustic signatures corresponding to the operating scene (200, 400, 600, 700, 800, 900) and the operating situation, and comparing the signatures with collected and classified acoustic waveforms; or c) retrieving seismic signatures corresponding to the operating scene (200, 400, 600, 700, 800, 900) and the operating situation, and comparing the signatures with collected and classified seismic waveforms.

Description

The description refers to situational awareness in road vehicles. Vehicle systems are known to monitor the vehicle's surroundings to enhance the driver's situational awareness; examples include forward and reverse distance, distance rate, and vision systems. Such systems can be used to provide the driver with warnings and control inputs regarding objects, including other vehicles. These systems can be employed in autonomous and semi-autonomous vehicle control systems, such as adaptive cruise control, parking assistance, lane keeping assist, and blind spot warnings for adjacent lanes. However, known system capabilities and implementations primarily focus on line-of-sight detection. The US 2007 / 0 286 475 A1 describes an object detection unit for detecting an object present around the vehicle using a plurality of sensors, comprising: an existence probability calculation unit configured to calculate each existence probability of the object for each sensor based on a normal distribution centered on an output value of each sensor; an existence probability correction unit configured to calculate each corrected existence probability by correcting each of the aforementioned existence probabilities with a detection rate of each sensor; and a fusion existence probability calculation unit configured to calculate a fusion existence probability of the object by fusing each of the aforementioned corrected existence probabilities. The DE 10 2009 006 113 A1 Describes a device and a method for providing an environment representation of a vehicle, comprising at least one first sensor device and at least one second sensor device, as well as an evaluation device. The sensor devices provide information about objects detected in the vehicle's environment in the form of sensor objects. A sensor object represents an object detected by the respective sensor device. The sensor objects include, as an attribute, at least one probability of existence of the represented object. The sensor objects detected by the at least one first sensor device and the at least one second sensor device are subjected to object fusion, generating fusion objects to which at least one probability of existence is assigned as an attribute. The probabilities of existence of the fusion objects are fused based on the probabilities of existence of the sensor objects. The fusion of the probability of existence of one of the sensor objects depends on the respective sensor device from which the corresponding sensor object is provided. The DE 10 2019 205 607 A1 This describes a predictive lane-change system to assist a host vehicle currently in a lane and adjacent to a neighboring lane. The predictive lane-change system may include an identification module that identifies a potential lane-change location and receives data on nearby vehicles associated with it. The predictive lane-change system may include a forecasting module that predicts future kinematic data for a number of nearby vehicles at a future time. The predictive lane-change system may include a determination module that determines whether a gap will be available at the potential lane-change location at that future time, based on the future kinematic data. The predictive lane-change system may include a lane-change module that, in response to a determination that a gap will be available at the potential lane-change location at a future time, initiates a lane-change maneuver for the host vehicle. It can be considered a task to specify an improved procedure and an improved system for determining the presence of a hidden danger. An inventive method for determining the presence of a hidden hazard via a controller comprises identifying an operating scene for a host vehicle based on information corresponding to the current geographic location of the host vehicle; identifying an operating situation for the host vehicle based on information corresponding to dynamic conditions within the operating scene; collecting and classifying information from a plurality of proximity sensors, wherein the proximity sensors are connected to the host vehicle and include an ambient light sensor, a vision system, an acoustic sensor, and a seismic sensor; and estimating a plurality of hidden hazard presence probabilities based on the information from each of the plurality of proximity sensors, the operating scene, the operating situation, and a comparison process. The process involves speaking and performing a fusion process with the multitude of hidden hazard presence probabilities to determine the presence of a hidden hazard. The comparison process includes: a) retrieving rules corresponding to the operational scene and situation, defining overlap and non-overlap zones within a headlight pattern of the host vehicle; creating brightness comparisons for the zones; and comparing these brightness comparisons with collected and classified areas of overlapping light within the host vehicle's headlight pattern; or b) retrieving acousti