DETECTION OF SNOW AND ICE ACCUMULATION ON A VEHICLE

Systems and methods for detecting snow and ice accumulation on a vehicle. In particular, systems and methods are provided for utilizing on-board sensors to automate the process of detecting snow and ice accumulation on surfaces of an autonomous vehicle. Additionally, systems and methods are provided for utilizing on-board sensors to monitor snow and ice accumulation on surfaces of an autonomous vehicle. Automating the detection and monitoring of snow and ice accumulation on a vehicle can minimize physical inspections and unnecessary interruptions to vehicle operation in wintry conditions.

BACKGROUND

1. Technical Field

The present disclosure generally relates to vehicle sensors and, more specifically, to sensing snow and/or ice accumulation on a vehicle.

An autonomous vehicle is a motorized vehicle that can navigate without a human driver. An exemplary autonomous vehicle can include various sensors, such as a camera sensor, a light detection and ranging (LIDAR) sensor, and a radio detection and ranging (RADAR) sensor, amongst others. The sensors collect data and measurements that the autonomous vehicle can use for operations such as navigation. The sensors can provide the data and measurements to an internal computing system of the autonomous vehicle, which can use the data and measurements to control a mechanical system of the autonomous vehicle, such as a vehicle propulsion system, a braking system, or a steering system. Typically, the sensors are mounted at fixed locations on the autonomous vehicles.

DETAILED DESCRIPTION

Overview

Systems and methods are provided for detecting snow and ice accumulation on a vehicle. In particular, systems and methods are provided for utilizing on-board sensors to automate the process of detecting snow and ice accumulation on surfaces of an autonomous vehicle. Additionally, systems and methods are provided for utilizing on-board sensors to monitor snow and ice accumulation on surfaces of an autonomous vehicle. Automating the detection and monitoring of snow and ice accumulation on a vehicle can minimize physical inspections and unnecessary interruptions to vehicle operation in wintry conditions.

In cold climates, vehicles are often exposed to situations where snow and/or ice accumulate on the surface of the vehicle and on vehicle sensors. Generally, a person can clear off the vehicle surfaces and/or sensors before driving. However, autonomous vehicles do not have a driver, and autonomous vehicle fleets can operate many autonomous vehicles with minimal interaction from fleet personnel. While personnel can be deployed to clear vehicles of ice and snow, in some cases only a portion of fleet vehicles may need to be cleared. In various examples, determining which vehicles have snow and/or ice accumulation on vehicle surfaces and/or sensors can help minimize manual labor for inspecting and/or clearing off the vehicles prior to vehicle deployment during and/or after a wintry event. Thus, automating the process of detecting snow and/or ice accumulation can improve operational efficiency of an autonomous vehicle fleet.

Used herein, ice may include snow (e.g., comprising ice crystals), frozen water, water that is frozen into a solid state, water in crystalline form, water in amorphous solid state, and water in solid form at or below 0 degrees Celsius. In some cases, ice may include ice in solid form and some water in liquid form. Ice may include water and other impurities.

Additionally, monitoring for snow and ice accumulation on a vehicle surface during operation allows for vehicles to return to a facility for cleaning when snow and ice accumulation is detected, rather than on a set schedule. In this manner, monitoring for snow and ice accumulation on a vehicle surface eliminates unnecessary interruptions to ridehail and delivery operations. Furthermore, some cities and/or states have regulations regarding vehicles driving with snow and ice on vehicle surfaces, and methods for autonomously monitoring for snow and ice accumulation on a vehicle surface can be used to ensure compliance with any local regulations.

Thus, systems and methods are provided herein for utilizing vehicle sensors to detect and monitor snow and ice accumulation on a vehicle. Additionally, vehicle communication systems can be used to communicate the presence of snow and ice on a vehicle. Various sensors that can be used to detect and monitor snow and ice accumulation include thermocouples, light sensors, cameras, microphones, accelerometers, and ultrasonic sensors. For example, a roof-mounted light sensor can be utilized to determine a level of light (lumen) measurement at the top of a vehicle. Similarly, cameras, such as autonomous vehicle perception cameras, can be used to determine a level of light measurement around the vehicle. In some examples, external mounted microphones can be used in conjunction with an acoustic processing module to detect and process sound, where attenuation of sound at the microphone can indicate snow and/or ice present on the surface at the microphone. In further examples, ultrasonic sensors can be used to detect snow and/or ice accumulation on the surface of the vehicle. Ultrasonic sensors can be used to detect near-range objects. Near-range objects can include objects that are less than two meters from the ultrasonic sensor. In some examples, one or more thermocouples can be used to determine a temperature, and a temperature measurement can be used to identify conditions in which snow and/or ice may be present. Similarly, a temperature measurement can be used to rule out the possibility of snow and/or ice causing various conditions at other sensors.

In some examples, accelerometers can be embedded into the body panels, fascias, and in features protruding from the vehicle. The accelerometers can detect acceleration at different points in the vehicle indicating added mass of snow and/or ice on the vehicle. The accelerometers can be installed around the vehicle body to collect acceleration information. The accelerometer data can be processed using digital signal processing techniques as well as with machine learning techniques to enhance the signals and extract features. Extracted features can include spectra shape and peak G feature. In various examples, the extracted features can be used to detect the added mass of snow and/or ice on the surfaces of the vehicle. In some implementations, data from embedded accelerometers and the IMU can be used to ascertain chassis response that signals the additional weight of snow and/or ice carried on the vehicle.

In some examples, sensor measurements across a local fleet can be averaged to determine a baseline average, such as an average of light measurements and/or an average of mass measurements. Then, sensor measurements at a particular vehicle can be compared to the baseline average to determine the relative magnitude of snow and/or ice accumulation for an individual vehicle.

In various implementations, systems and methods are provided for fusion of sensor data from embedded accelerometers, cameras, light sensors, microphones, ultrasonic detectors, and the inertial measurement unit (IMU) to identify the position and level of snow and ice accumulation on a vehicle. Additionally, machine learning models can be leveraged to determine the extent of snow and ice accumulation. In some examples, machine learning models can be used to improve accuracy of the determination of snow and ice accumulation.

Additionally, vehicle communication modules can be utilized to communicate instances of snow and ice accumulation. In particular, a vehicle perception module or a vehicle sensor stack signals to a wireless communication module the instance of snow and ice accumulation on the vehicle, the position of snow and ice accumulation on the vehicle, and the level of snow and ice accumulation on the vehicle. The onboard wireless communication module communicates the identification of snow and ice on the vehicle with a central computer (or back office). The central computer (or back office) monitors real time instances and severity of snow and/or ice accumulation across the vehicle fleet. In some examples, the central computer can schedule remediation activity based on the real time instances of snow and/or ice accumulation, and in some examples, the central computer can prioritize remediation activity based on the real time instances of snow and/or ice accumulation.

Example Vehicle for Detection of Ice and Snow Accumulation

FIGS.1A-1Billustrate autonomous vehicles110,130for detection of ice and snow accumulation, according to some examples of the present disclosure. The autonomous vehicle110includes a sensor suite102and an onboard computer104, and the autonomous vehicle130includes sensor suites122and an onboard computer124. In various implementations, the autonomous vehicles110,130uses sensor information from the sensor suites102,122to determine vehicle location, to navigate traffic, to sense and avoid obstacles, and to sense vehicle surroundings. The autonomous vehicles110,130uses sensor information from the sensor suites102,122to detect ice and/or snow accumulation on the vehicles110,130. In various examples, the vehicles110,130include additional sensors on the vehicle bodies, such as a rooftop sensor108,128. In some examples, the rooftop sensors108,128are ambient light sensors. According to various implementations, the autonomous vehicles110,130are part of a fleet of vehicles for picking up passengers and/or packages and driving to selected destinations. In some examples, the autonomous vehicles110,130are personal autonomous vehicles that are used by one or more owners for driving to selected destinations. In some examples, the autonomous vehicles110,130can connect with a central computer to download vehicle updates, maps, and other vehicle data.

The sensor suites102,122include localization and driving sensors. For example, the sensor suite102may include one or more of photodetectors, cameras, RADAR, sound navigation and ranging (SONAR), LIDAR, Global Positioning System (GPS), inertial measurement units (IMUs), accelerometers, microphones, strain gauges, pressure monitors, barometers, thermometers, altimeters, wheel speed sensors, and a computer vision system. The sensor suites102,122continuously monitor the autonomous vehicle's environment. In particular, the sensor suites102,122can be used to identify information and determine various factors regarding an autonomous vehicle's environment. In some examples, data from the sensor suite102,122can be used to update a map with information used to develop layers with waypoints identifying various detected items. Additionally, sensor suite102,122data can provide localized traffic information, ongoing road work information, and current road condition information. Furthermore, sensor suite102,122data can provide current environmental information, including roadside environment or parking area environment information and information about other nearby vehicles when parked, as well as the presence of people, crowds, and/or objects on a roadside, sidewalk, or parking area. In this way, sensor suite102,122data from many autonomous vehicles can continually provide feedback to the mapping system and a high fidelity map can be updated as more and more information is gathered. Additionally, sensor suite102,122data can provide local weather information as well as information about snow and/or ice accumulation on the vehicles110,130, respectively. In some examples, the sensor suite102includes one or more thermocouples which can be used to measure temperature. A temperature measurement can be used to determine the possibility of snow and/or ice on the vehicle110,130.

In various examples, the sensor suites102,122include cameras implemented using high-resolution imagers with fixed mounting and field of view. In further examples, the sensor suite102,122includes LIDARs implemented using scanning LIDARs. Scanning LIDARs have a dynamically configurable field of view that provides a point cloud of the region intended to scan. In still further examples, the sensor suite102,122includes RADARs implemented using scanning RADARs with dynamically configurable field of view. In some examples, the sensor suites102,122can include one or more microphones, ultrasonic sensors, accelerometers, light sensors, and mass sensors.

The autonomous vehicles110,130each include an onboard computer104,124which functions to control the autonomous vehicle110,130. The onboard computer104,124processes sensed data from the sensor suite102,122and/or other sensors, in order to determine a state of the autonomous vehicle110,130. Additionally, the onboard computer104,124processes sensed data from the sensor suite102,122as well as sensor data from other vehicle sensors to detect snow and ice accumulation on the vehicles110,130. In some examples, the onboard computer104,124checks for vehicle updates from a central computer or other secure access point. In some examples, a vehicle sensor log receives and stores processed sensed sensor suite102,122data from the onboard computer104,124. In some examples, a vehicle sensor log receives sensor suite102,122data from the sensor suite102,122. The vehicle sensor log can be used to determine a state of a vehicle and various maintenance items such as charging, cleaning, and potential vehicle damage.

In some implementations described herein, the autonomous vehicles110,130include sensors inside the vehicle. In some examples, the autonomous vehicles110,130include one or more cameras inside the vehicle. The cameras can be used to detect items or people inside the vehicle. In some examples, the autonomous vehicles110,130include one or more weight sensors inside the vehicle, which can be used to detect items or people inside the vehicle. In some examples, the interior sensors can be used to detect passengers inside the vehicle. Additionally, based upon the vehicle state and programmed instructions, the onboard computer104,124controls and/or modifies driving behavior of the autonomous vehicle110,130.

The onboard computer104,124functions to control the operations and functionality of the autonomous vehicle110,130and processes sensed data from the sensor suite102,122and/or other sensors in order to determine states of the autonomous vehicle and to detect snow and ice accumulation on the autonomous vehicle110,130. In some implementations, the onboard computer104,124is a general purpose computer adapted for I/O communication with vehicle control systems and sensor systems. In some implementations, the onboard computer104,124is any suitable computing device. In some implementations, the onboard computer104,124is connected to the Internet via a wireless connection (e.g., via a cellular data connection). In some examples, the onboard computer104,124is coupled to any number of wireless or wired communication systems. In some examples, the onboard computer104,124is coupled to one or more communication systems via a mesh network of devices, such as a mesh network formed by autonomous vehicles.

According to various implementations, the autonomous driving systems100,120ofFIGS.1A,1Bfunction to enable an autonomous vehicle110,130to modify and/or set a driving behavior in response to parameters set by vehicle passengers (e.g., via a passenger interface). Driving behavior of an autonomous vehicle may be modified according to explicit input or feedback (e.g., a passenger specifying a maximum speed or a relative comfort level), implicit input or feedback (e.g., a passenger's heart rate), or any other suitable data or manner of communicating driving behavior preferences.

The autonomous vehicle110.130is preferably a fully autonomous automobile, but may additionally or alternatively be any semi-autonomous or fully autonomous vehicle. In various examples, the autonomous vehicle110,130is a boat, an unmanned aerial vehicle, a driverless car, a golf cart, a truck, a van, a recreational vehicle, a train, a tram, a three-wheeled vehicle, a bicycle, a scooter, a tractor, a lawn mower, a commercial vehicle, an airport vehicle, or a utility vehicle. Additionally, or alternatively, the autonomous vehicles may be vehicles that switch between a semi-autonomous state and a fully autonomous state and thus, some autonomous vehicles may have attributes of both a semi-autonomous vehicle and a fully autonomous vehicle depending on the state of the vehicle.

In various implementations, the autonomous vehicle110,130includes a throttle interface that controls an engine throttle, motor speed (e.g., rotational speed of electric motor), or any other movement-enabling mechanism. In various implementations, the autonomous vehicle110,130includes a brake interface that controls brakes of the autonomous vehicle110,130and controls any other movement-retarding mechanism of the autonomous vehicle110,130. In various implementations, the autonomous vehicle110,130includes a steering interface that controls steering of the autonomous vehicle110,130. In one example, the steering interface changes the angle of wheels of the autonomous vehicle. The autonomous vehicle110,130may additionally or alternatively include interfaces for control of any other vehicle functions, for example, windshield wipers, headlights, turn indicators, air conditioning, etc.

Ice and Snow Accumulation Detection Module

FIG.2is a block diagram illustrating a snow and ice detection module202, according to various examples of the present disclosure. The snow and ice detection module202can be configured to detect the accumulation of snow and/or ice on a surface of a vehicle. The surface can be any vehicle surface that is near horizontal, such as the roof, hood, or trunk. Near horizontal surfaces can include surfaces on which snow can accumulate. In some examples, near horizontal surfaces include surfaces that have less than a twenty degree incline with respect to horizontal. In some examples, the surface can be any vehicle surface, such as a window, windshield, door, headlight, or bumper. In some examples, the snow and ice detection module can determine the level of snow and/or ice accumulation at different positions on the vehicle. In various examples, the snow and ice detection module202can distinguish between snow and/or ice on the vehicle versus dirt and/or dust occluding a vehicle sensor.

According to various implementations, the snow and ice detection module202receives input sensor data from various sensors. In some examples, the snow and ice detection module202processes the sensor data to determine a presence of snow and/or ice on the vehicle. In some examples, the sensor data is pre-processed before being transmitted to the snow and ice detection module202, and the snow and ice detection module202uses the pre-processed data to detect the presence of snow and/or ice on the vehicle.

The sensor data input to the snow and ice detection module202can include one or more of camera data from a camera204, light sensor data from a light sensor206, IMU data from an IMU208, accelerometer data from an accelerometer210, microphone data from a microphone212, and ultrasound data from an ultrasound sensor214. Additionally, the snow and ice detection module202can receive baseline measurements216. The baseline measurements can include recent measurements from other fleet vehicles.

The IMU data from the IMU208can include lateral and longitudinal acceleration measurements as well as linear velocity, torque, and angular rate. In various examples, the IMU can include one or more accelerometers and one or more gyroscopes. The IMU data can be used to determine the inertia of the vehicle chassis, including measurements of roll, pitch, and yaw of the vehicle chassis. In some examples, the IMU data can be used by the snow and ice detection module202to determine the mass of the snow and/or ice on the vehicle. In particular, the additional weight of the snow and ice on the vehicle affects the vehicle dynamics, such as vehicle acceleration and torque. In particular, the additional mass of the snow and ice affects lateral and longitudinal acceleration. In some examples, the additional mass of snow and ice can act on vehicle suspension. In some examples, increased weight on the vehicle can decrease an acceleration response. In various examples, the additional mass of snow and ice can affect the roll, pitch, and yaw of the vehicle chassis, and the differing roll, pitch and yaw offsets can be used to detect the presence of snow and/or ice on the vehicle, as well as to determine the mass of the snow and ice on the vehicle. In various examples, the roll of the chassis is the side-to-side motion of the chassis, the pitch of the chassis is the front-to-back motion of the chassis, and the yaw of the chassis is the rotating motion of the chassis. In some examples, the snow and ice detection module202determines whether the IMU data includes measurements that have crossed a selected threshold, indicating the presence of snow and/or ice on the vehicle. In some examples, the snow and ice detection module202also receives data on the torque used to accelerate the vehicle, where an increased torque measurement can indicate additional weight and the presence of ice and/or snow on the vehicle.

In various implementations, the snow and ice detection module202has knowledge of other additional weight in the vehicle. For example, if the vehicle has picked up a passenger or a delivery, the additional weight of the passenger or delivery is determined when the passenger enters the vehicle or when the delivery is placed in the vehicle, and the baseline weight of the vehicle is adjusted to account for the additional weight of the passenger or delivery. The baseline weight is used to determine expected chassis response. In contrast, snow and/or ice on a vehicle surface accumulates slowly over time, and the actual chassis response is used to determine the mass of the additional snow and/or ice.

In various examples, the snow and ice detection module202receives additional sensor data from various vehicle sensors. In some examples, additional sensor data can increase the accuracy of the snow and ice detection module202in detecting snow and/or ice on the vehicle. In some examples, the snow and ice detection module202receives other sensor data from various vehicle sensors, and the detection of snow and/or ice is based on data from other sensors.

In some implementations, the snow and ice detection module202receives acceleration data from one or more embedded accelerometers210. The accelerometers210can be embedded in the vehicle body panels, vehicle fascia, other vehicle elements, and in other features protruding from the vehicle, to provide acceleration data at different points in the vehicle. In various examples, the accelerometers are installed around the vehicle body to collect acceleration information at different points on the vehicle. In some examples, the acceleration data from the embedded accelerometers210can be used in conjunction with the IMU data to detect the presence of snow and/or ice on the vehicle. In particular, acceleration data can be used to determine a response of the vehicle chassis to attempted acceleration, and thereby determine a weight of snow and ice on the vehicle. In some examples, the acceleration data from the embedded accelerometers210can be used in conjunction with the IMU data to determine the thickness or depth of snow and/or ice on the vehicle. In various examples, the accelerometer data can be processed using conventional digital signal processing techniques as well as with machine learning techniques to enhance the signals and extract features, such as spectra shape and peak G feature. The snow and ice detection module202can use the processed accelerometer data to detect the added mass of snow or ice on the surfaces of the vehicle.

In some implementations, the snow and ice detection module202receives light sensor data from the light sensor206. The light sensor data can include the intensity of light received at the sensor. The light sensor data can include a lumen measurement. In some examples, the light sensor206is located on a vehicle roof. In some examples, a vehicle uses the light sensor206to determine when to automatically illuminate vehicle headlights. In particular, in some examples, when the light intensity received at the light sensor206drops below a selected threshold, the vehicle determines that the ambient light level is low and illuminates the headlights. In various implementations, the light intensity at the light sensor206can decrease if the light sensor becomes dirty or snow and/or ice occlude the light sensor206.

Additional vehicle sensors can be used to determine the cause of decreased light intensity at the light sensor. For example, if dirt and/or dust occlude the light sensor206, similar dirt and/or dust likely occlude other vehicle sensors such as vehicle cameras204and ultrasound sensors214. However, when the light sensor206is positioned on a vehicle surface that is horizontal or near horizontal, snow and/or ice can occlude the light sensor206while sensors that are located on vertical and/or diagonal surfaces remain clear. Thus, in some examples, when snow and/or ice accumulate on a vehicle, a first lumen measurement based on data from the light sensor206can be compared to a second lumen measurement based on data from a camera204to determine that the light sensor206is occluded with snow and/or ice. The camera204can be positioned in a vehicle sensor suite, such as the sensor suites102and122ofFIGS.1A and1B, or the camera204can be located in another near vertical surface of the vehicle, such as in a vehicle bumper, a vehicle side, a vehicle rear, a vehicle front, or a vehicle door. In some examples, data from multiple vehicle perception cameras can be used to measure a level of light around the vehicle.

In some examples, additional sensor data can be used by the snow and ice detection module to determine the presence of snow and/or ice decreasing the light intensity at the light sensor206, such as temperature data, and fleet baseline measurements216. According to various examples, the lower the level of light at the light sensor206(the lower the lumen measurement) on the roof, the greater the thickness of the snow and/or ice on the roof and occluding the light sensor206. In particular, increased depth of snow and/or ice can be estimated based on the lower the level of light at the light sensor206as compared to the level of light at a vehicle camera.

In some implementations, an exterior vehicle microphone212can be used to detect an attenuation of received acoustic data indicating snow and/or ice is present on the microphone212. In particular, snow and/or ice on the microphone can cause an attenuation in microphone data. An acoustic processing module can be included in the snow and ice detection module202, or the acoustic processing module can receive the microphone212data, process the microphone212data, and transmit the processed microphone data to the snow and ice detection module. The snow and ice detection module202can use the processed microphone data to detect the presence of snow and/or ice on the vehicle.

In some implementations, the snow and ice detection module202receives ultrasound data from an ultrasound sensor214. Ultrasound sensors214can be used to detect near-range objects, and in particular, ultrasound sensors214can be used to detect objects that are less than two meters from the vehicle. In some examples, ultrasound sensors214can detect snow and/or ice accumulated on the sensor214.

In various implementations, the snow and ice detection module202receives baseline measurements216from a central computer or back office. The baseline measurements216can be used to determine a baseline average light level (lumen measurement) and a baseline average mass measurement. The baseline measurements216can be used in determining a relative magnitude of snow and/or ice accumulation for an individual vehicle. The baseline measurements216can include measurements from other vehicles in the fleet, and the baseline measurements can include an average of measurements from other vehicles in the fleet. In some examples, the baseline measurements include measurements from other fleet vehicles that are located within a selected distance of the vehicle in which the snow and ice detection module202is located. In various examples, the snow and ice detection module202can compare its measurements to the baseline measurements216. In one example, a baseline lumen measurement for other nearby fleet vehicles can provide information about ambient light, which can be used to compare with the light intensity at the light sensor206. In some examples, the snow and ice detection module also maintains its own database of baseline vehicle measurements, such that it has typical sensor data measurements for comparison with current and/or new data.

In various implementations, the snow and ice detection module202can utilize fusion of data from the IMU, embedded accelerometers, cameras, light sensors, microphones, and/or ultrasonic detection, to identify the instance, position, and level of snow and ice accumulation on the vehicle. Additionally, the snow and ice detection module202can leverage machine learning models to more accurately determine the extent of snow and/or ice accumulation.

In various implementations, the snow and ice detection module202can be used to monitor snow and ice accumulation over time. In particular, sensor data can be periodically updated and snow and ice detection measurements can be periodically updated. Updated measurements can be compared to older measurements to detect changes over time, as well as to determine an overall accumulation of snow and/or ice over time.

In various implementations, the snow and ice detection module202includes a communication module for communicating with a central computer. In particular, the snow and ice detection module202can communicate current snow and ice accumulation data with a central computer, such as when snow and/or ice has been detected on the vehicle and/or a level of snow and/or ice accumulation on the vehicle. Additionally, the snow and ice detection module202can communicate sensor data and/or measurements based on the sensor data with a central computer. In some examples, the snow and ice detection module202communicates with an onboard computer, and the onboard computer communicates with the central computer.

According to various implementations, when snow and/or ice is detected on a vehicle, remedial actions can be taken to remove the snow and/or ice from the vehicle before the vehicle resumes operation. Remedial actions include actions taken by the vehicle itself, such as heating a vehicle surface to melt ice and/or snow, and/or driving to a facility for remedial action. Remedial actions can include pulling over and/or parking the vehicle.

Method for Detection of Ice and Snow Accumulation

FIG.3is a flow chart illustrating a method300for detection of snow and ice accumulation on a vehicle, according to various examples of the present disclosure. At step302, light sensor data is received from an external vehicle light sensor. As discussed above, the vehicle light sensor can be located in a vehicle roof. The vehicle light sensor can be used to determine ambient light levels. In some examples, the vehicle light sensor is used to determine when ambient light levels have decreased below a selected level and illuminate vehicle headlights. At step304, accelerometer data is received from an embedded accelerometer. In various examples, accelerometer data is received from multiple embedded accelerometers located at different places on the vehicle. At step306, inertial measurement data is received from an inertial measurement unit (IMU). The IMU can include an accelerometer and a gyroscope, and can provide data on the inertia of the vehicle chassis. IMU data can include lateral and longitudinal acceleration measurements as well as linear velocity, torque, and angular rate.

At step308, a lumen measurement is determined based on the light sensor data. The light sensor lumen measurement can indicate the intensity of light at the light sensor. At step310, an acceleration measurement is determined based on the accelerometer data. In particular, when accelerometer data is received from multiple accelerometers, the acceleration of various parts of the vehicle can be determined and compared. The accelerometer data can be combined with the IMU data to determine lateral and longitudinal acceleration of the vehicle. At step312, IMU measurements including a linear velocity and an angular rate are determined. In various examples, the IMU measurements include roll, pitch, and yaw of the vehicle chassis. In some examples, the IMU measurements include a roll offset, a pitch offset, and a yaw offset of the vehicle chassis, where the offset indicates a change from typical roll, pitch, and yaw values.

At step314, a chassis response of the vehicle chassis is determined based on the IMU measurements and the acceleration measurements. In some examples, the IMU data can be used to determine measurements including roll, pitch, and yaw of the vehicle chassis. In various examples, snow and/or ice on a vehicle weighs the vehicle down and delays the chassis response to attempted vehicle acceleration. At step316, the mass of the ice and the snow on the vehicle can be estimated based on the chassis response. Similarly, the weight of the ice and the snow on the vehicle can be estimated based on the chassis response.

At step318, a thickness of the ice and the snow on the vehicle based on the lumen measurement. In particular, in various examples, the lumen measurement can be compared to a light intensity measurement from another light sensor and/or from a vehicle perception camera to determine the decrease in light intensity at the light sensor due to the presence of snow and/or ice on the light sensor. At step320, ice and/or snow accumulation is detected based on the estimated mass of the ice and snow and the estimated thickness of the ice and snow.

In various examples, the ice and snow accumulation, including the estimated mass and the estimated thickness, is communicated with a central computer. In some examples, the snow and ice detection module can receive a baseline lumen measurement from the central computer. Similarly, in some examples, the snow and ice detection module can receive a baseline mass measurement from the central computer. The baseline lumen measurement and the baseline mass measurement can be based on fleet data or on data from a selected portion of a vehicle fleet.

In various implementations, the method300can be repeated to monitor ice and snow accumulation over time. When snow and/or ice are detected on a vehicle, remedial actions to remove the snow and/or ice can be initiated.

Example of an Autonomous Vehicle Fleet System for Snow and Ice Detection

FIG.4is a diagram400illustrating a fleet of autonomous vehicles410a,410b,410cin communication with a central computer402, according to some embodiments of the disclosure. The vehicles410a-410ccommunicate wirelessly with a cloud404and a central computer402. The central computer402includes a routing coordinator, a dispatch service, and a database of information from the vehicles410a-410cin the fleet. In various examples, the vehicles410a-410ccommunicate snow and ice detection data with the central computer402. In some examples, the database of information can include snow and/or ice accumulation data as well as other sensor data such as light intensity at the light sensor of each vehicle410a,410b,410c. The central computer402can monitor snow and ice accumulation across the vehicles410a-410cin the fleet to schedule and prioritize remediation activities.

Autonomous vehicle fleet routing refers to the routing of multiple vehicles in a fleet. The central computer402also communicates with various vehicle facilities such as the vehicle facility406. In some examples, the dispatch system at the central computer402can communicate a service instruction to any of the vehicles410a-410c. In some examples, snow and/or ice has accumulated on a vehicle410a-410c, and the snow and/or ice needs to be cleared off the vehicle410a-410c. The dispatch system can then route the vehicle410a-410cto a facility406for service including snow and ice removal.

As described above, each vehicle410a-410cin the fleet of vehicles communicates with a routing coordinator. Thus, information gathered by various autonomous vehicles410a-410cin the fleet can be saved and used to generate information for future routing determinations. For example, sensor data can be used to generate route determination parameters. In general, the information collected from the vehicles in the fleet can be used for route generation or to modify existing routes. In some examples, the routing coordinator collects and processes position data from multiple autonomous vehicles in real-time to avoid traffic and generate a fastest-time route for each autonomous vehicle. In some implementations, the routing coordinator uses collected position data to generate a best route for an autonomous vehicle in view of one or more traveling preferences and/or routing goals. In some examples, the routing coordinator uses collected position data corresponding to emergency events to generate a best route for an autonomous vehicle to avoid a potential emergency situation and associated unknowns. In some examples, the routing coordinator generates a route for a vehicle to the facility406. In some examples, a vehicle has one or more scheduled stops before embarking on its route to the facility406.

Example Autonomous Vehicle Management System

In this example, the AV management system500includes an AV502, a data center550, and a client computing device570. The AV502, the data center550, and the client computing device570can communicate with one another over one or more networks (not shown), such as a public network (e.g., the Internet, an Infrastructure as a Service (IaaS) network, a Platform as a Service (PaaS) network, a Software as a Service (Saas) network, another Cloud Service Provider (CSP) network, etc.), a private network (e.g., a Local Area Network (LAN), a private cloud, a Virtual Private Network (VPN), etc.), and/or a hybrid network (e.g., a multi-cloud or hybrid cloud network, etc.).

AV502can navigate about roadways without a human driver based on sensor signals generated by multiple sensor systems504,506, and508. The sensor systems504-508can include different types of sensors and can be arranged about the AV502. For instance, the sensor systems504-508can comprise Inertial Measurement Units (IMUs), cameras (e.g., still image cameras, video cameras, etc.), light sensors (e.g., LIDAR systems, ambient light sensors, infrared sensors, etc.), RADAR systems, a Global Navigation Satellite System (GNSS) receiver, (e.g., Global Positioning System (GPS) receivers), audio sensors (e.g., microphones, Sound Navigation and Ranging (SONAR) systems, ultrasonic sensors, etc.), engine sensors, speedometers, tachometers, odometers, altimeters, tilt sensors, impact sensors, airbag sensors, seat occupancy sensors, open/closed door sensors, tire pressure sensors, rain sensors, and so forth. For example, the sensor system504can be a camera system, the sensor system506can be a LIDAR system, and the sensor system508can be a RADAR system. Other embodiments may include any other number and type of sensors. In various examples, the sensor systems can be used to detect snow and/or ice on the AV502, and the sensor systems can be used to detect snow and/or ice accumulation on the AV502. In some examples, a snow and ice detection module580can receive data from the sensor systems504,506,508and detect snow and/or ice on the vehicle based on the received data.

AV502can also include several mechanical systems that can be used to maneuver or operate AV502. For instance, the mechanical systems can include vehicle propulsion system530, braking system532, steering system534, safety system536, and cabin system538, among other systems. Vehicle propulsion system530can include an electric motor, an internal combustion engine, or both. The braking system532can include an engine brake, a wheel braking system (e.g., a disc braking system that utilizes brake pads), hydraulics, actuators, and/or any other suitable componentry configured to assist in decelerating AV502. The steering system534can include suitable componentry configured to control the direction of movement of the AV502during navigation. Safety system536can include lights and signal indicators, a parking brake, airbags, and so forth. The cabin system538can include cabin temperature control systems, in-cabin entertainment systems, and so forth. In some embodiments, the AV502may not include human driver actuators (e.g., steering wheel, handbrake, foot brake pedal, foot accelerator pedal, turn signal lever, window wipers, etc.) for controlling the AV502. Instead, the cabin system538can include one or more client interfaces (e.g., Graphical User Interfaces (GUIs), Voice User Interfaces (VUIs), etc.) for controlling certain aspects of the mechanical systems530-538.

AV502can additionally include a local computing device510that is in communication with the sensor systems504-508, the mechanical systems530-538, the data center550, and the client computing device570, among other systems. The local computing device510can include one or more processors and memory, including instructions that can be executed by the one or more processors. The instructions can make up one or more software stacks or components responsible for controlling the AV502; communicating with the data center550, the client computing device570, and other systems; receiving inputs from riders, passengers, and other entities within the AV's environment; logging metrics collected by the sensor systems504-508; and so forth. In this example, the local computing device510includes a perception stack512, a mapping and localization stack514, a planning stack516, a control stack518, a communications stack520, a High Definition (HD) geospatial database522, and an AV operational database524, among other stacks and systems.

Perception stack512can enable the AV502to “see” (e.g., via cameras, LIDAR sensors, infrared sensors, etc.), “hear” (e.g., via microphones, ultrasonic sensors, RADAR, etc.), and “feel” (e.g., pressure sensors, force sensors, impact sensors, etc.) its environment using information from the sensor systems504-508, the mapping and localization stack514, the HD geospatial database522, other components of the AV, and other data sources (e.g., the data center550, the client computing device570, third-party data sources, etc.). The perception stack512can detect and classify objects and determine their current and predicted locations, speeds, directions, and the like. In addition, the perception stack512can determine the free space around the AV502(e.g., to maintain a safe distance from other objects, change lanes, park the AV, etc.). The perception stack512can also identify environmental uncertainties, such as where to look for moving objects, flag areas that may be obscured or blocked from view, and so forth. The perception stack512can be used in sentinel mode to sense the vehicle environment and identify objects.

Mapping and localization stack514can determine the AV's position and orientation (pose) using different methods from multiple systems (e.g., GPS, IMUs, cameras, LIDAR, RADAR, ultrasonic sensors, the HD geospatial database522, etc.). For example, in some embodiments, the AV502can compare sensor data captured in real-time by the sensor systems504-508to data in the HD geospatial database522to determine its precise (e.g., accurate to the order of a few centimeters or less) position and orientation. The AV502can focus its search based on sensor data from one or more first sensor systems (e.g., GPS) by matching sensor data from one or more second sensor systems (e.g., LIDAR). If the mapping and localization information from one system is unavailable, the AV502can use mapping and localization information from a redundant system and/or from remote data sources.

The planning stack516can determine how to maneuver or operate the AV502safely and efficiently in its environment. For example, the planning stack516can receive the location, speed, and direction of the AV502, geospatial data, data regarding objects sharing the road with the AV502(e.g., pedestrians, bicycles, vehicles, ambulances, buses, cable cars, trains, traffic lights, lanes, road markings, etc.) or certain events occurring during a trip (e.g., an Emergency Vehicle (EMV) blaring a siren, intersections, occluded areas, street closures for construction or street repairs, Double-Parked Vehicles (DPVs), etc.), traffic rules and other safety standards or practices for the road, user input, and other relevant data for directing the AV502from one point to another. The planning stack516can determine multiple sets of one or more mechanical operations that the AV502can perform (e.g., go straight at a specified speed or rate of acceleration, including maintaining the same speed or decelerating; turn on the left blinker, decelerate if the AV is above a threshold range for turning, and turn left; turn on the right blinker, accelerate if the AV is stopped or below the threshold range for turning, and turn right; decelerate until completely stopped and reverse; etc.), and select the best one to meet changing road conditions and events. If something unexpected happens, the planning stack516can select from multiple backup plans to carry out. For example, while preparing to change lanes to turn right at an intersection, another vehicle may aggressively cut into the destination lane, making the lane change unsafe. The planning stack516could have already determined an alternative plan for such an event, and upon its occurrence, help to direct the AV502to go around the block instead of blocking a current lane while waiting for an opening to change lanes.

The control stack518can manage the operation of the vehicle propulsion system530, the braking system532, the steering system534, the safety system536, and the cabin system538. The control stack518can receive sensor signals from the sensor systems504-508as well as communicate with other stacks or components of the local computing device510or a remote system (e.g., the data center550) to effectuate operation of the AV502. For example, the control stack518can implement the final path or actions from the multiple paths or actions provided by the planning stack516. This can involve turning the routes and decisions from the planning stack516into commands for the actuators that control the AV's steering, throttle, brake, and drive unit.

The communication stack520can transmit and receive signals between the various stacks and other components of the AV502and between the AV502, the data center550, the client computing device570, and other remote systems. The communication stack520can enable the local computing device510to exchange information remotely over a network, such as through an antenna array or interface that can provide a metropolitan WIFI® network connection, a mobile or cellular network connection (e.g., Third Generation (3G), Fourth Generation (4G), Long-Term Evolution (LTE), 5th Generation (5G), etc.), and/or other wireless network connection (e.g., License Assisted Access (LAA), Citizens Broadband Radio Service (CBRS), MULTEFIRE, etc.). The communication stack520can also facilitate local exchange of information, such as through a wired connection (e.g., a user's mobile computing device docked in an in-car docking station or connected via Universal Serial Bus (USB), etc.) or a local wireless connection (e.g., Wireless Local Area Network (WLAN), Bluetooth®, infrared, etc.).

The AV operational database524can store raw AV data generated by the sensor systems504-508and other components of the AV502and/or data received by the AV502from remote systems (e.g., the data center550, the client computing device570, etc.). In some embodiments, the raw AV data can include HD LIDAR point cloud data, image or video data, RADAR data, GPS data, and other sensor data that the data center550can use for creating or updating AV geospatial data as discussed further below with respect toFIG.5and elsewhere in the present disclosure.

The data center550can be a private cloud (e.g., an enterprise network, a co-location provider network, etc.), a public cloud (e.g., an Infrastructure as a Service (laaS) network, a Platform as a Service (PaaS) network, a Software as a Service (Saas) network, or other Cloud Service Provider (CSP) network), a hybrid cloud, a multi-cloud, and so forth. The data center550can include one or more computing devices remote to the local computing device510for managing a fleet of AVs and AV-related services. For example, in addition to managing the AV502, the data center550may also support a ridesharing service, a delivery service, a remote/roadside assistance service, street services (e.g., street mapping, street patrol, street cleaning, street metering, parking reservation, etc.), and the like.

The data center550can send and receive various signals to and from the AV502and the client computing device570. These signals can include sensor data captured by the sensor systems504-508, roadside assistance requests, software updates, ridesharing pick-up and drop-off instructions, and so forth. In this example, the data center550includes one or more of a data management platform552, an Artificial Intelligence/Machine Learning (AI/ML) platform554, a simulation platform556, a remote assistance platform558, a ridesharing platform560, and a map management platform562, among other systems.

Data management platform552can be a “big data” system capable of receiving and transmitting data at high speeds (e.g., near real-time or real-time), processing a large variety of data, and storing large volumes of data (e.g., terabytes, petabytes, or more of data). The varieties of data can include data having different structures (e.g., structured, semi-structured, unstructured, etc.), data of different types (e.g., sensor data, mechanical system data, ridesharing service data, map data, audio data, video data, etc.), data associated with different types of data stores (e.g., relational databases, key-value stores, document databases, graph databases, column-family databases, data analytic stores, search engine databases, time series databases, object stores, file systems, etc.), data originating from different sources (e.g., AVs, enterprise systems, social networks, etc.), data having different rates of change (e.g., batch, streaming, etc.), or data having other heterogeneous characteristics. The various platforms and systems of the data center550can access data stored by the data management platform552to provide their respective services.

The AI/ML platform554can provide the infrastructure for training and evaluating machine learning algorithms for operating the AV502, the simulation platform556, the remote assistance platform558, the ridesharing platform560, the map management platform562, and other platforms and systems. Using the AI/ML platform554, data scientists can prepare data sets from the data management platform552; select, design, and train machine learning models; evaluate, refine, and deploy the models; maintain, monitor, and retrain the models; and so on.

The simulation platform556can enable testing and validation of the algorithms, machine learning models, neural networks, and other development efforts for the AV502, the remote assistance platform558, the ridesharing platform560, the map management platform562, and other platforms and systems. The simulation platform556can replicate a variety of driving environments and/or reproduce real-world scenarios from data captured by the AV502, including rendering geospatial information and road infrastructure (e.g., streets, lanes, crosswalks, traffic lights, stop signs, etc.) obtained from the map management platform562; modeling the behavior of other vehicles, bicycles, pedestrians, and other dynamic elements; simulating inclement weather conditions, different traffic scenarios; and so on.

The remote assistance platform558can generate and transmit instructions regarding the operation of the AV502. For example, in response to an output of the AI/ML platform554or other system of the data center550, the remote assistance platform558can prepare instructions for one or more stacks or other components of the AV502.

The ridesharing platform560can interact with a customer of a ridesharing service via a ridesharing application572executing on the client computing device570. The client computing device570can be any type of computing system, including a server, desktop computer, laptop, tablet, smartphone, smart wearable device (e.g., smart watch; smart eyeglasses or other Head-Mounted Display (HMD); smart ear pods or other smart in-ear, on-ear, or over-ear device; etc.), gaming system, or other general purpose computing device for accessing the ridesharing application572. The client computing device570can be a customer's mobile computing device or a computing device integrated with the AV502(e.g., the local computing device510). The ridesharing platform560can receive requests to be picked up or dropped off from the ridesharing application572and dispatch the AV502for the trip.

In some embodiments, the map viewing services of map management platform562can be modularized and deployed as part of one or more of the platforms and systems of the data center550. For example, the AI/ML platform554may incorporate the map viewing services for visualizing the effectiveness of various object detection or object classification models, the simulation platform556may incorporate the map viewing services for recreating and visualizing certain driving scenarios, the remote assistance platform558may incorporate the map viewing services for replaying traffic incidents to facilitate and coordinate aid, the ridesharing platform560may incorporate the map viewing services into the client application572to enable passengers to view the AV502in transit en route to a pick-up or drop-off location, and so on.

FIG.6illustrates an example processor-based system with which some aspects of the subject technology can be implemented. For example, processor-based system600can be any computing device making up, or any component thereof in which the components of the system are in communication with each other using connection605. Connection605can be a physical connection via a bus, or a direct connection into processor610, such as in a chipset architecture. Connection605can also be a virtual connection, networked connection, or logical connection.

Example system600includes at least one processing unit (Central Processing Unit (CPU) or processor)610and connection605that couples various system components including system memory615, such as Read-Only Memory (ROM)620and Random-Access Memory (RAM)625to processor610. Computing system600can include a cache of high-speed memory612connected directly with, in close proximity to, or integrated as part of processor610.

Processor610can include any general-purpose processor and a hardware service or software service, such as services632,634, and636stored in storage device630, configured to control processor610as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor610may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. In some examples, the processor610is an image processor that can process images from vehicle image sensors. In some examples, the processor610can determine a sensor field of view. In some examples, the processor610can stitch together captured images from adjacent image sensors.

Storage device630can include software services, servers, services, etc., that when the code that defines such software is executed by the processor610, it causes the system600to perform a function. In some embodiments, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor610, connection605, output device635, etc., to carry out the function.

Selected Examples

Example 1 provides a vehicle for detection of ice and snow accumulation, comprising an inertial measurement unit (IMU) configured to generate IMU data, wherein the IMU includes at least one IMU accelerometer and a gyroscope; a plurality of embedded accelerometers configured to generate accelerometer data; and an ice and snow detector configured to: receive the accelerometer data and the IMU data, identify a chassis response based on the accelerometer data and the IMU data; determine a weight of ice and snow on the vehicle based on the chassis response; and determine an accumulation of ice and snow on the vehicle based on the weight determination.

Example 2 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, further comprising an external light sensor, wherein the external light sensor is located on a vehicle roof, and wherein the ice and snow detector is further configured to: receive light sensor data from the external light sensor, determine a lumen measurement based on the light sensor data, and estimate a thickness of ice and snow on the vehicle roof based on the lumen measurement.

Example 3 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, further comprising an external vehicle camera, and wherein the ice and snow detector is further configured to: receive camera data from the external vehicle camera, determine a light intensity measurement based on the camera data, and compare the lumen measurement with the light intensity measurement to generate a comparison, wherein estimating the thickness of the ice and the snow on the vehicle roof includes estimating a thickness of the ice and the snow on the vehicle based in part on the comparison.

Example 4 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, wherein the ice and snow detector is further configured to communicate the determination of the accumulation of ice and snow on the vehicle with a central computer.

Example 5 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, wherein the ice and snow detector is further configured to receive baseline measurements from the central computer, wherein the baseline measurements include a baseline ambient light measurement.

Example 6 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, further comprising an external microphone, and an acoustic processing module, wherein the acoustic processing module is configured to receive microphone data and determine a sound attenuation measurement, and wherein the ice and snow detector is further configured to: receive the sound attenuation measurement, and detect the accumulation of ice and snow based, in part, on the attenuation measurement.

Example 7 provides a method for detection of ice and snow accumulation on a vehicle comprising: receiving inertial measurement data from an inertial measurement unit (IMU); receiving accelerometer data from an embedded accelerometer; determining IMU measurements including a linear velocity and an angular rate; determining an acceleration measurement based on the accelerometer data; determining a chassis response of a vehicle chassis based on the IMU measurements and the acceleration measurement; estimating a mass of the ice and the snow on the vehicle based on the chassis response; and detecting the ice and snow accumulation based on the estimated mass of the ice and snow.

Example 8 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, further comprising: receiving light sensor data from an external vehicle light sensor; determining a lumen measurement based on the light sensor data; and estimating a thickness of the ice and the snow on the vehicle based on the lumen measurement.

Example 9 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, wherein the lumen measurement is a first lumen measurement and further comprising: capturing camera data with an external vehicle camera; determining a second lumen measurement based on the camera data; and comparing the first lumen measurement with the second lumen measurement to generate a comparison; and wherein estimating the thickness of the ice and the snow on the vehicle includes estimating a thickness of the ice and the snow on the vehicle based in part on the comparison.

Example 10 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, wherein receiving light sensor data includes receiving first light sensor data at a first time, receiving accelerometer data includes receiving first accelerometer data at the first time, and receiving inertial measurement data includes receiving first inertial measurement data at the first time, wherein the lumen measurement is a first lumen measurement, the acceleration measurement is a first acceleration measurement, and the IMU measurements are first IMU measurements, and further comprising: monitoring the ice and snow accumulation to identify a rate of the ice and snow accumulation, wherein monitoring includes receiving, at a second time, second light sensor data, second accelerometer data, and second inertial measurement data, and determining a change in the ice and snow accumulation between the first time and the second time.

Example 11 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, further comprising communicating the ice and snow accumulation, including the estimated mass and the estimated thickness, with a central computer.

Example 12 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, further comprising receiving a baseline lumen measurement from the central computer and receiving a baseline mass measurement, wherein the baseline lumen measurement and the baseline mass measurement are based on fleet data.

Example 13 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, wherein detecting the ice and snow accumulation includes at least one of: determining the estimated mass of the ice and the snow exceeds a selected mass threshold, and determining the estimated thickness of the ice and the snow exceeds a selected thickness threshold.

Example 14 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, further comprising receiving audio data from an external vehicle microphone, and identifying attenuation based on the audio data, and wherein detecting the ice and snow accumulation further including detecting the ice and snow accumulation based on the attenuation.

Example 15 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, further comprising utilizing a machine learning model for detecting the ice and snow accumulation.

Example 16 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, further comprising utilizing the machine learning model for estimating the thickness of the ice and snow accumulation.

Example 17 provides a system for detection of ice and snow accumulation on a vehicle, comprising: a plurality of vehicles, each vehicle including: a respective inertial measurement unit (IMU) configured to generate IMU data, wherein the IMU includes at least one IMU accelerometer and a gyroscope; a respective plurality of embedded accelerometers configured to generate accelerometer data; and a respective ice and snow detector configured to: receive the accelerometer data and the IMU data, identify a chassis response based on the accelerometer data and the IMU data; determine a weight of ice and snow on the vehicle based on the chassis response; and determine an accumulation of ice and snow on the vehicle based on the weight determination; and a central computer in communication with each of the plurality of vehicles, and configured to generate baseline measurements for ice and snow detection.

Example 18 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, wherein each of the plurality of vehicles further includes a respective external light sensor, wherein the respective external light sensor is located on a respective vehicle roof, and wherein the respective ice and snow detector is further configured to: receive light sensor data from the respective external light sensor, determine a lumen measurement based on the light sensor data, and estimate a thickness of ice and snow on the respective vehicle roof based on the lumen measurement.

Example 19 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, wherein each of the plurality of vehicles further includes a respective external vehicle camera, and wherein the respective ice and snow detector in each respective vehicle is further configured to: receive camera data from the respective external vehicle camera, determine a light intensity measurement based on the camera data, and compare the lumen measurement with the light intensity measurement to generate a comparison, wherein estimating the thickness of the ice and the snow on the respective vehicle roof includes estimating a thickness of the ice and the snow on the respective vehicle based in part on the comparison.

Example 20 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, wherein the respective ice and snow detectors are further configured to: communicate the determination of the accumulation of ice and snow on the respective vehicle with the central computer, and receive the baseline measurements from the central computer.

Example 20 provides a method, system, and/or vehicle according to any of the preceding and/or following examples, further comprising a thermocouple for determining a temperature measurement, wherein the temperature measurement can be used to identify a possibility of a presence of ice and snow.

Example 22 includes a vehicle comprising means for performing the method of any of the examples 1-20.

Example 23 provides that a method of any of examples 1-21 is a computer-implemented method.