Patent Application
20250014466
This disclosure relates to a method for distracted driver detection and alert
Distracted driving constitutes any activity that diverts a driver's attention away from the road such as using wireless devices, interacting with a vehicle's infotainment system, talking to passengers, etc. A distracted driver disrupts the flow of traffic, increases travel time for fellow drivers, compromises road safety, and could contribute to instances of road rage. Drivers are most likely to be distracted when stopped at a red light and/or stuck in traffic. In these scenarios, the distracted driver may not react appropriately to the flow of traffic (e.g., the driver does not begin driving when a traffic signal changes from red to green) which may cause additional congestion and/or result in collisions as other drivers attempt to circumvent the distracted driver.
One aspect of the disclosure provides a computer-implemented method for distracted driver detection and alert during stop and go traffic in a vehicle that may include a conventional internal combustion engine vehicle, a hybrid electric vehicle, a fuel cell vehicle, and/or a battery electric vehicle that incorporates a front proximity sensor. The computer-implemented method executed on data processing hardware that causes the data processing hardware to perform operations including, during the operation of the vehicle, receiving vehicle speed data from one or more wheel speed sensors disposed on the vehicle and receiving proximity data from one or more proximity sensors disposed on the vehicle, the proximity data indicating a distance of the vehicle relative to any objects in front of the vehicle. The operations include executing a distracted driver detection algorithm that uses the vehicle speed data and the proximity data to determine whether the vehicle is disrupting a flow of stop and go traffic. The operations also including executing the distracted driver detection algorithm that uses the vehicle speed data and the proximity data to determine whether a driver of the vehicle is distracted from operating the vehicle. The operations include based on determining that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted, instructing a system of the vehicle to output an alert to reengage the driver.
Implementations of the disclosure may include one or more of the following optional features. In some implementations, executing the distracted driver detection algorithm determines that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted without using any data obtained from driver monitoring equipment and without using any data obtained from advanced driver-assistance systems (ADAS) equipment. Further, instructing the system of the vehicle to output the alert to reengage the driver may include instructing an infotainment system of the vehicle to audibly output an audible alert from an acoustic speaker of the vehicle, the acoustic speaker in communication with the data processing hardware. Alternatively, instructing the system of the vehicle to output the alert to reengage the driver may include instructing an infotainment system of the vehicle to visually output a graphical alert on a display screen of the vehicle, the display screen in communication with the data processing hardware. In some implementations, instructing the system of the vehicle to output the alert to reengage the driver includes instructing one or more interior components of the vehicle to output a haptic alert.
In some implementations, executing the distracted driver detection algorithm includes determining the vehicle is disrupting the flow of stop and go traffic based on the vehicle speed data indicating that a speed of the vehicle is less than a threshold speed. Executing the distracted driver detection algorithm in these implementations further includes determining the driver of the vehicle is distracted from operating the vehicle based on the proximity data indicating that the distance of the vehicle relative to a second vehicle in front of the vehicle is greater than a threshold distance or a rate of change of the distance of the vehicle relative to the second vehicle in front of the vehicle is greater than a threshold rate of change.
In some implementations, the operations further include receiving drive state data indicating that a drive gear of the vehicle is actuated. In these implementations, executing the distracted driver detection algorithm to determine that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted is further based on the drive state data indicating that the drive gear of the vehicle is actuated.
In other implementations, the operations include receiving parking brake state data indicating that a parking brake of the vehicle is released. In these implementations, executing the distracted driver detection algorithm to determine that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted is further based on the parking brake state data indicating that the parking brake of the vehicle is released.
In still other implementations, the operations include receiving a hazard light state indication indicating that hazard lights of the vehicle are off. In these implementations, executing the distracted driver detection algorithm to determine that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted is further based on the hazard light state indication indicating that hazard lights of the vehicle are off.
In some implementations, the operations include receiving a power mode indication indicating that a power mode of the vehicle includes a propulsion mode. In these implementations, executing the distracted driver detection algorithm to determine that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted is further based on the power mode indication indicating that the power mode of the vehicle includes the propulsion mode.
The vehicle may include any of a battery electric vehicle, a hybrid electric vehicle, or an internal combustion engine. In some implementations, the data processing hardware resides on the vehicle.
Another aspect of the disclosure provides a system for distracted driver detection and alert during stop and go traffic in a vehicle that may include a conventional internal combustion engine vehicle, a hybrid electric vehicle, a fuel cell vehicle, and/or a battery electric vehicle that incorporates a front proximity sensor. The system includes data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that when executed on the data processing hardware cause the data processing hardware to perform operations. The operations include during the operation of the vehicle, receiving vehicle speed data from one or more wheel speed sensors disposed on the vehicle and receiving proximity data from one or more proximity sensors disposed on the vehicle, the proximity data indicating a distance of the vehicle relative to any objects in front of the vehicle. The operations include executing a distracted driver detection algorithm that uses the vehicle speed data and the proximity data to determine whether the vehicle is disrupting a flow of stop and go traffic. The operations also including executing the distracted driver detection algorithm that uses the vehicle speed data and the proximity data to determine whether a driver of the vehicle is distracted from operating the vehicle. The operations include based on determining that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted, instructing a system of the vehicle to output an alert to reengage the driver.
This aspect may include one or more of the following optional features. Implementations of the disclosure may include one or more of the following optional features. In some implementations, executing the distracted driver detection algorithm determines that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted without using any data obtained from driver monitoring equipment and without using any data obtained from advanced driver-assistance systems (ADAS) equipment. Further, instructing the system of the vehicle to output the alert to reengage the driver may include instructing an infotainment system of the vehicle to audibly output an audible alert from an acoustic speaker of the vehicle, the acoustic speaker in communication with the data processing hardware. Alternatively, instructing the system of the vehicle to output the alert to reengage the driver may include instructing an infotainment system of the vehicle to visually output a graphical alert on a display screen of the vehicle, the display screen in communication with the data processing hardware. In some implementations, instructing the system of the vehicle to output the alert to reengage the driver includes instructing one or more interior components of the vehicle to output a haptic alert.
In some implementations, executing the distracted driver detection algorithm includes determining the vehicle is disrupting the flow of stop and go traffic based on the vehicle speed data indicating that a speed of the vehicle is less than a threshold speed. Executing the distracted driver detection algorithm in these implementations further includes determining the driver of the vehicle is distracted from operating the vehicle based on the proximity data indicating that the distance of the vehicle relative to a second vehicle in front of the vehicle is greater than a threshold distance or a rate of change of the distance of the vehicle relative to the second vehicle in front of the vehicle is greater than a threshold rate of change.
In some implementations, the operations further include receiving drive state data indicating that a drive gear of the vehicle is actuated. In these implementations, executing the distracted driver detection algorithm to determine that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted is further based on the drive state data indicating that the drive gear of the vehicle is actuated.
In other implementations, the operations include receiving parking brake state data indicating that a parking brake of the vehicle is released. In these implementations, executing the distracted driver detection algorithm to determine that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted is further based on the parking brake state data indicating that the parking brake of the vehicle is released.
In still other implementations, the operations include receiving a hazard light state indication indicating that hazard lights of the vehicle are off. In these implementations, executing the distracted driver detection algorithm to determine that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted is further based on the hazard light state indication indicating that hazard lights of the vehicle are off.
In some implementations, the operations include receiving a power mode indication indicating that a power mode of the vehicle includes a propulsion mode. In these implementations, executing the distracted driver detection algorithm to determine that both the vehicle is disrupting the flow of stop and go traffic and the driver of the vehicle is distracted is further based on the power mode indication indicating that the power mode of the vehicle includes the propulsion mode.
The vehicle may include any of a battery electric vehicle, a hybrid electric vehicle, or an internal combustion engine. In some implementations, the data processing hardware resides on the vehicle.
The details of one or more implementations of the disclosure are set forth in the accompanying drawings and the description below. Other aspects, features, and advantages will be apparent from the description and drawings, and from the claims.
Like reference symbols in the various drawings indicate like elements.
Drivers are becoming increasingly distracted with the rise of smartphones and complex infotainment systems. Distracted driving is problematic as it contributes to longer traffic times, compromises road safety, and is a major cause of collisions. One way to prevent distracted driving is by detecting and alerting a distracted driver through visual, audible, or haptic feedback. Current distracted driver detection systems incorporate complex Advanced Driver Assistance Systems (ADAS) equipment (e.g., camera modules, Radar, Lidar) integrated with object detection, traffic sign detection, and driver monitoring algorithms. Such equipment is generally expensive and requires sophisticated software for accurate operation, making the equipment less suitable for aftermarket integration into existing vehicles. The complexity and cost of the components used by current distracted driver detection systems make these systems less accessible.
Implementations herein are directed to a distracted driver detection system and method for detecting a distracted driver and providing alerts during stop and go traffic. In particular, the distracted driver detection system of the current disclosure monitors a state of a vehicle during operation to detect a distracted driver disrupting a flow of traffic based on an arbitration of vehicle data. The vehicle data of the vehicle includes any data collected by sensors of the vehicle that can be used to determine if a driver is distracted, such as proximity data, speed data, gear data, power data, control data, etc. The distracted driver detection system of the current disclosure relies on conventional sensors that are available in most vehicles (e.g., wheel speed sensors and/or proximity sensors) without implementing complex and expensive ADAS equipment. By utilizing components and equipment that are already included in most vehicles, the distracted driver detection system of the current disclosure is more accessible than known systems and more easily integrated into most vehicles (i.e., no additional hardware is necessary).
Referring to
A battery or energy storage device (ESD) 180 of the vehicle 105 supplies the electric power for operating the distracted driver detection and alert system 100. In some examples, the vehicle 105 includes an electrically powered or battery powered vehicle and the ESD 180 electrically powers multiple systems of the vehicle 105, such as a drive system and the distracted driver detection and alert system 100 of the vehicle 105. Optionally, the ESD 180 includes an auxiliary battery or a dedicated battery for powering only the distracted driver detection and alert system 100.
The distracted driver detection module 250 executes on data processing hardware 510 (
The one or more vehicle sensors 122 may be deployed throughout the vehicle 105. For example, one or more proximity sensors 122, 122B are deployed in the front of the vehicle 105 and one or more wheel speed sensors 112, 122A are deployed at the wheels of the vehicle 105. Further, any other sensors 122 that can be deployed in a vehicle 105 to collect vehicle data indicative of the operation of the vehicle 105 can be used by the distracted driver detection module 250. Other example sensors 122 include GPS, body controller, powertrain domain controller, electronic parking brake, odometer, accelerometer, light sensors, power mode sensors, hazard light state, etc. The drive modes 124 may include any data related to the actuated gear of the vehicle (e.g., park, reverse, neutral, drive). The distracted driver detection and alert system 100 may be configured to detect the distracted driver without using any data obtained from driver monitoring equipment and without using any data obtained from Advanced Driver-Assistance Systems (ADAS) equipment.
The alert module 260 is electrically coupled to the ESD 180 and the driver interface system 270 to control one or more alerts at the driver interface system 270 based on signals from the distracted driver detection module 250. The distracted driver detection module 250 and the alert module 260 are configured to cause one or more alerts at the driver interface system 270 of the distracted driver detection and alert system 100. For example, the distracted driver detection module 250 determines, based on vehicle data from the one or more vehicle sensors 122, that the driver of the vehicle is distracted. In this example, the distracted driver detection module 250 transmits a signal (e.g., distracted driver state 251 of
The wheel speed sensors 122, 122A can include any sensors that can measure the speed of the vehicle 105. For example, the wheel speed sensors 122A may be a speedometer, an odometer, an accelerometer, etc. In some implementations, the wheel speed sensors 122A are deployed at or near the wheels of the vehicle 105 and measure the speed of the vehicle 105 based on the rotations of the wheels. In some implementations, when the vehicle 105 is not moving, the vehicle 105 may be in a vehicle standstill state 203. In other words, the vehicle 105, may be determined to have a speed of zero or near zero.
The drive modes 124 of the vehicle may refer to the actuated gear state and/or the drive state data indicating which drive gear of the vehicle 105 is actuated. For example, the drive mode 124 may indicate that the vehicle 105 is in park, reverse, neutral, drive, or low gear.
The front proximity sensors 122, 122B may be used to determine proximity data indicating a distance between the vehicle 105 and any objects (e.g., other vehicles) ahead of the vehicle 105. Further, the front proximity sensors 122B can be used to determine the change of distance over time between the vehicle 105 and any objects ahead of the vehicle 105. For example, when the distance between the vehicle 105 and the object ahead of the vehicle 105 is constantly increasing and decreasing in measure, the distracted driver detection module 250 is more likely to determine that the driver is distracted (i.e., that the distracted driver state 251 is true). Alternatively, when the distance between the vehicle 105 and the object ahead of the vehicle 105 is relatively constant in measure, the distracted driver detection module 250 is more likely to determine that the driver is not distracted (i.e., that the distracted driver state 251 is false). The front proximity sensor 122B may include any known sensor for measuring distance.
The electronic parking brake 122, 122C provides the parking brake state data indicating the position of the parking brake of the vehicle 105. The distracted driver detection module 250 may determine that the driver is distracted when the parking brake of the vehicle 105 is activated and the distance between the vehicle 105 and an object ahead of the vehicle 105 is increasing. Alternatively, distracted driver detection module 250 may determine that the driver is less likely to be distracted when the parking brake of the vehicle 105 is released.
The powertrain domain controller 122, 122D may indicate a power mode of the vehicle. In particular, the powertrain domain controller 122D may indicate whether the vehicle 105 is or is not in propulsion mode. Further, the powertrain domain controller 122D may indicate any other known or applicable power mode of the vehicle 105.
The body controller 122, 122E of the vehicle 105 may provide a hazard light state indication indicating the state of the hazard lights of the vehicle 105. In some implementations, the body controller 122E provides an indication of any other lights of the vehicle such as the brake lights, high beams, fog lights, interior lights, headlights, etc.
The distracted driver detection module 250 may obtain and/or receive data from the sensors 122A-E and 124. In some implementations, the distracted driver detection module 250 constantly obtains vehicle data from the sensors 122A-E and 124 throughout operation of the vehicle 105 to determine whether the driver of the vehicle is distracted. In other implementations, the distracted driver detection module 250 is only activated when the vehicle 105 accelerates and/or decelerates a threshold number of times within a time period (i.e., indicating that the vehicle 105 is in stop and go traffic). In some implementations, the distracted driver detection module 250 implements an algorithm (300 of
Based on the distracted driver state 251, the alert module 260 may cause one or more components 270A-C of the driver interface system 270 to alert the driver of the vehicle 105. For example, the alert module 260 causes the visual component 270A to display a graphical alert on a display screen of the vehicle 105 indicating that the driver is distracted. In another example, the alert module 260 causes the audible component 270B to audibly output an audible alert from an acoustic speaker of the vehicle 105 indicating that the driver is distracted. In yet another example, the alert module 260 causes the haptic component 270C to output a haptic alert by an interior component of the vehicle 105 indicating that the driver is distracted.
The examples described above with respect to
The example algorithm 300 of
The computing device 500 includes a processor 510, memory 520, a storage device 530, a high-speed interface/controller 540 connecting to the memory 520 and high-speed expansion ports 550, and a low speed interface/controller 560 connecting to a low speed bus 570 and a storage device 530. Each of the components 510, 520, 530, 540, 550, and 560, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 510 can process instructions for execution within the computing device 500, including instructions stored in the memory 520 or on the storage device 530 to display graphical information for a graphical user interface (GUI) on an external input/output device, such as display 580 coupled to high speed interface 540. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 500 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
The memory 520 stores information non-transitorily within the computing device 500. The memory 520 may be a computer-readable medium, a volatile memory unit(s), or non-volatile memory unit(s). The non-transitory memory 520 may be physical devices used to store programs (e.g., sequences of instructions) or data (e.g., program state information) on a temporary or permanent basis for use by the computing device 500. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM)/programmable read-only memory (PROM)/erasable programmable read-only memory (EPROM)/electronically erasable programmable read-only memory (EEPROM) (e.g., typically used for firmware, such as boot programs). Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase change memory (PCM) as well as disks or tapes.
The storage device 530 is capable of providing mass storage for the computing device 500. In some implementations, the storage device 530 is a computer-readable medium. In various different implementations, the storage device 530 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. In additional implementations, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 520, the storage device 530, or memory on processor 510.
The high speed controller 540 manages bandwidth-intensive operations for the computing device 500, while the low speed controller 560 manages lower bandwidth-intensive operations. Such allocation of duties is exemplary only. In some implementations, the high-speed controller 540 is coupled to the memory 520, the display 580 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 550, which may accept various expansion cards (not shown). In some implementations, the low-speed controller 560 is coupled to the storage device 530 and a low-speed expansion port 590. The low-speed expansion port 590, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet), may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
The computing device 500 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 500a or multiple times in a group of such servers 500a, as a laptop computer 500b, or as part of a rack server system 500c.
Various implementations of the systems and techniques described herein can be realized in digital electronic and/or optical circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A software application (i.e., a software resource) may refer to computer software that causes a computing device to perform a task. In some examples, a software application may be referred to as an “application,” an “app,” or a “program.” Example applications include, but are not limited to, system diagnostic applications, system management applications, system maintenance applications, word processing applications, spreadsheet applications, messaging applications, media streaming applications, social networking applications, and gaming applications.
These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, non-transitory computer readable medium, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.
The processes and logic flows described in this specification can be performed by one or more programmable processors, also referred to as data processing hardware, executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, one or more aspects of the disclosure can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.
A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims.
This U.S. patent application claims priority under 35 U.S.C. § 119 (e) to U.S. Provisional Application 63/512,022, filed on Jul. 5, 2023. The disclosure of this prior application is considered part of the disclosure of this application and is hereby incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63512022 | Jul 2023 | US |
