This document relates to remote vehicle monitoring and control.
Autonomous vehicle navigation is a technology for sensing the position and movement of a vehicle and, based on the sensing, autonomously control the vehicle to navigate towards a destination. Autonomous vehicle navigation can have important applications in transportation of people, goods and services.
Disclosed are devices, systems and methods for remote safe driving, which include self-checking systems, emergency handling systems, and remote control systems. In an example, this may be achieved by a remote monitor center controlling some part of the monitoring and emergency handling services on the vehicle, and providing commands to ensure the safety of the vehicle and its passengers in the case of an emergency.
In one aspect, the disclosed technology can be used to provide a method for remote safe driving of a vehicle. This method may be implemented at the vehicle. The method includes detecting an emergency situation, and in response to the detecting the emergency situation, switching operation of the vehicle to a low-power operation mode that comprises shutting down a subset of vehicular components, and periodically transmitting a location of the vehicle to a remote monitoring center.
In another aspect, the disclosed technology can be used to provide another method for remote safe driving of a vehicle. This method may be implemented at a remote data center that is in communication with the vehicle. The method includes selecting at least one of a set of vehicular driving actions, and transmitting, over a secure connection, the at least one of the set of vehicular driving actions to the vehicle, wherein the set of vehicular driving actions is generated based on a classification of a plurality of driver behavior.
In yet another example aspect, a computing apparatus that includes a processor for implementing one of the methods recited herein is disclosed.
In yet another example aspect, a computer program product comprising a computer-readable program medium having code stored thereon is disclosed. The code, when executed by a processor, causes the processor to implement a method as described.
The above and other aspects and features of the disclosed technology are described in greater detail in the drawings, the description and the claims.
Autonomous vehicles use a variety of techniques to detect their surroundings, such as radar, laser light, GPS, odometry and computer vision. Control systems may interpret sensory information to identify appropriate navigation paths, as well as planned and unplanned obstacles and relevant signage along the route. The remote driving of vehicles may further rely on monitoring and classification systems that are capable of analyzing sensory data to distinguish between a variety of factors, e.g. different weather conditions, different cars on the road, and different obstacles.
Another integral feature of autonomous driving should be the safety of the vehicle and its passengers, as well as the safety of neighboring people and property. Thus, autonomous vehicles should be equipped with emergency handling systems to ensure the safe driving of the vehicles, especially when performed remotely. The response to an emergency situation should be rapid and precise, since safety is paramount. One of the main goals to enable widespread use of autonomous vehicles is to achieve and exceed the reliability of human driving behavior, and remote safe driving is integral to this goal. The techniques described in the present document can be incorporated in embodiments of a fully-autonomous vehicle, a semi-autonomous vehicle and/or a control center that controls operation of the autonomous vehicle. In particular, using the disclosed techniques, upon detection of an abnormality, an autonomous vehicle may safely stop further driving and ask for assistance. Similarly, in some embodiments, when a control center becomes aware of an autonomous vehicle's distress condition, the control center may provide the vehicle with further instructions to safely cease driving and wait for further assistance. These, and other, features are further described herein.
In some embodiments, the emergency handling system defines several emergency status conditions, and corresponding autonomous vehicle actions for each status condition. For example, the emergency status conditions may include:
(a) a “truck/vehicle abnormal” status, which may include a CAN (Controller Area Network) bus response signal indicating any abnormality,
(b) a “sensor abnormal” status, which may indicate that abnormalities with any of the sensors including the camera, radar, GPS, and inertial measurement unit (IMU) have been detected, e.g., a lack of a signal from one or more of these sensors,
(c) an “ECU abnormal” status, which is a self-detection status check for the hardware components of the vehicle,
(d) a “system abnormal” status, which may indicate a problem with the middleware, e.g., a middleware system deadlock that lasts for more than 3 seconds,
(e) a “network abnormal” status, which may indicate a problem with the network, e.g., the network is disconnected,
(f) a “car control fail” status, which indicates a failure in at least one self-detection test of the control module, e.g., no output or an abnormal output,
(g) a “planning fail” status, which indicates a failure in at least one self-detection test of the planning module, e.g., no output or an abnormal output,
(h) a “perception fail” status, which indicates a failure in at least one self-detection test of the perception module, e.g., no output or an abnormal output, and
(i) a “localization fail” status, which indicates a failure in at least one self-detection test of the localization module, e.g., no output or an abnormal output.
In some embodiments, the vehicle may stop in an emergency lane when the “truck (or vehicle) abnormal” status indicator is detected, and may stop in the lane it is currently driving in when any of the other status indicator is detected.
In some embodiments, one or more of the enumerated emergency status conditions may be transmitted to the remote data center over a secure connection as soon as they are detected as part of an emergency signal. In other embodiments, the emergency status conditions may be transmitted as part of periodically transmitted monitoring signals. In yet other embodiments, a semi-persistent approach may be adopted, where periodic monitoring updates are transmitted from the vehicle to the remote data center, but an emergency signal transmission takes precedence and is transmitted as soon as it is generated.
In some embodiments, the remote data center may receive the necessary emergency signals from the vehicle over a dedicated and secure emergency channel. In one example, the status of the vehicle may be derived from the emergency signals received. In another example, the remote data center may receive the status of the vehicle from the vehicle itself, as part of the communication that contained the necessary emergency signals, or in a separate communication. In yet another example, the status condition (which may be a non-emergency or emergency status condition) may be accompanied by a corresponding report providing additional information related to that status condition.
In some embodiments, the emergency signals include a location and a vehicle status message. In an example, the location may be specified in absolute or relative coordinates. The vehicle status message may include a status indicator and specific information elements. In some embodiments, the status indicators may have levels or tiers, as shown in the example table below:
In some embodiments, each of the enumerated status conditions may take on a value shown in the example table above, and may be transmitted to the remote data center, along with any corresponding information elements that may be required. In some embodiments, the operation of the vehicle, the generation of an emergency status, and the response required to resolve the emergency situation may be implemented as shown in the state diagram in
Upon detecting an emergency condition, the exemplary procedure for remote safe driving may implement one of at least two policy strategies. The first policy 230 dictates that the vehicle should search for the nearest emergency lane, and safely come to a stop in the emergency lane. The second policy 250 dictates that the vehicle stop in the lane it is currently operating in (referred to as the “self-lane”). For example, and under this policy, the vehicle may determine that an immediate stop may be required, and that the driving or emergency conditions may preclude taking the time to search for and move to an emergency lane.
When the vehicle has stopped in the emergency lane 230, the vehicle may take appropriate measures to address the emergency situation, and then restart from the emergency lane 240 (and referred to a Process I). Similarly, when the vehicle has stopped in the self-lane 250, it may restart from the self-lane 260 (referred to as Process II) after appropriate measures have been taken. Restarting operations from either the emergency lane or the self-lane returns the state of the vehicle to the “running” state 210, as shown in
In some embodiments, the hardware self-checking process 345 may include self-checking one or more of the ECU module (e.g., CPU, GPU, memory, mainboard), the sensor module (e.g., camera, radar, IMU, GPS sensor), and the power module (e.g., converter system), and reporting the results of each subsystem self-check to the data center 310.
In some embodiments, the software self-checking process 355 may include self-checking the Octopus platform, which is an open-source platform for graph-based program analysis. The software self-checking process may further include self-checking the algorithms modules (e.g., maps and localization, perception, control, motion planning).
In some embodiments, the surrounding checking process 365 may ensure other vehicles, objects and/or persons in the vicinity of the vehicle are accounted for prior to restarting.
In some embodiments, the autonomous driving process 375 includes bringing the vehicle into a semi- or fully-autonomous driving mode.
In an example, the sensor system 420 may include a CAN bus sensor, a camera, radar capabilities, a GPS unit and/or an IMU, and Lidar capabilities. In another example, the middleware system 440 may include the system module, and the algorithm module 460 may include a localization module, a perception module, a control module, and a planning module.
In some embodiments, a monitoring system (e.g. the monitoring system 114 shown in
In some embodiments, the vehicle status message may be defined as including the following subfields, one or more of which may be transmitted at each time:
(1) vehicle running status as a 1-bit field with a “0” indicating that the vehicle is running and a “1” indicating that the vehicle has stopped;
(2) vehicle self-status using the standard or extended frame formats (as described in CAN 2.0 A and CAN 2.0 B);
(3) hardware status defined as:
(4) system status defined as:
(5) algorithm status defined as:
The message formats shown above are exemplary, and other formats with different lengths for the bitfields, as well as additional bitfields and status indicators, are envisioned as part of the disclosed technology.
Embodiments of the disclosed technology may be advantageously implemented in a modular fashion to support both fully-autonomous as well as semi-autonomous vehicles. For example, a semi-autonomous vehicle that is actively and safely being controlled by a driver may not need to implement autonomous driving (e.g., the autonomous driving process 375 in
The method includes, at step 520, switching, in response to the detecting, operation of the vehicle to a low-power operation mode that comprises shutting down a subset of vehicular components. In some embodiments, the subset of vehicular components include non-essential sensors and subsystems that are non-emergency subsystems. Since recovering from the emergency situation is integral to the safety of the vehicle and its passengers, and to people and property in the vicinity of the vehicle, subsystems that are not required to resolve the emergency situation are turned off in order to ensure enough power is available for critical subsystems. In an example, the non-emergency subsystems may include the vehicle entertainment subsystem, and map and navigation support for retail establishments and points of interest.
In some embodiments, subsystems may correspond to the sensor system, the middleware system and/or the algorithm module (as shown in
As discussed in the context of the state diagram shown in
In some embodiments, performing a status check an restarting (or starting) certain components may be based on the changing environment. For an example, if it starts to get dark while the vehicle remains on the side of the road, a status check may be performed on the hazard lights, which may then be turned on to ensure the visibility of the vehicle. For another example, if rush hour starts and parking restrictions are imposed in the right-most lane in which the vehicle is parked, a status check may be performed on the autonomous driving system (ADS), and the vehicle carefully driven to an alternate safe spot.
The method includes, at step 530, periodically transmitting, in response to the detecting, a location of the vehicle to a remote monitoring center. In some embodiments, as soon as any emergency status condition is detected, the vehicle may periodically transmit its GPS coordinates (or location relative to known mile markers, other landmarks, or Wi-Fi transmitters) to the remote data center. In an example, the period of the transmission of the vehicle location may be much shorter than a period typically used for transmitting monitoring status updates.
In some embodiments, cargo being hauled by the vehicle may be critically important to a customer and is deemed an essential component when an emergency is detected. For example, when the vehicle is transitioning to a low-power operating mode in which non-essential and non-emergency components are shut down, power may be routed to the cargo container to ensure that it is maintained at a predetermined thermal profile. The periodic transmission of the location of the vehicle will advantageously enable the remote center (e.g., 130 in
For example, the classification may be a clustering algorithm that uses a data set of driver behavior, which may be used to train the algorithm to identify the aforementioned vehicular driving actions. The clustering algorithm may be a hierarchical clustering algorithm, a centroid-based clustering algorithm, a distribution-based clustering algorithm, or other such supervised learning algorithms.
The method 600 includes, at step 620, transmitting, over a secure connection, the at least one of the set of vehicular driving actions to the vehicle. In some embodiments, the secure connection may be the emergency channel (or link) exclusively that is reserved for emergency communications. In other embodiments, the secure connection may be an operational channel (or link) that is typically used for high-speed data communications.
In some embodiments, the operational link may be secured using an Internet layer cryptographic protocol, which enforces authenticity, integrity and secrecy. In an example, the operational link may use the Internet Protocol security (IPsec) protocol, pre-shared keys (PSK) or a public-key cryptosystem, e.g. RSA or Diffie-Hellman key exchange. The emergency channel may be secured using a low-latency Application layer cryptographic protocol, due to the imperative nature of emergency communications. In an example, the Secure Sockets Layer (SSL) protocol or the Transport Layer Security (TLS) protocol may be used for the emergency channel, which due to the time sensitive nature of the messages, does not require explicit client authentication after an initial authentication process.
Embodiments of the disclosed technology can be configured to implement solutions for remote safe driving as discussed in this document. These solutions include:
1. A method for remote safe driving of a vehicle, comprising: detecting an emergency situation; and in response to the detecting, switching operation of the vehicle to a low-power operation mode that comprises shutting down a subset of vehicular components; and periodically transmitting a location of the vehicle to a remote monitoring center.
2. The method of solution 1, wherein the emergency situation comprises an abnormal sensor operation, an abnormal electronic control unit (ECU) operation, an abnormal network operation, a communication failure, a failure of a planning module, a failure of a car control module, a failure of a localization module, or a failure of a perception module.
3. The method of solution 1 or 2, wherein the subset of vehicular components comprises non-essential sensors and non-emergency subsystems.
4. The method of any of solutions 1 to 3, further comprising: stopping the vehicle due to the emergency situation; shutting down and restarting another subset of vehicular components; and performing a status check on at least one of the another subset of vehicular components.
In some solutions, the other set of vehicular components that are restarted may be selected in response to changes in the external environment. For an example, if the vehicle has come to a stop and dusk is approaching, the hazard lights will be turned on to ensure the visibility of the vehicle by other vehicles.
5. The method of solution 4, wherein the vehicle stops in a lane in which the vehicle is operating.
6. The method of solution 4, wherein stopping the vehicle comprises: finding an emergency lane; and stopping the vehicle in the emergency lane.
7. The method of any of solutions 4 to 6, further comprising: selectively restarting the operation of the vehicle based on a result of the status check.
8. The method of any of solutions 1 to 7, wherein the location of the vehicle comprises at least one of Global Positioning System (GPS) coordinates, a location relative to mile markers, or a location relative to known Wi-Fi transmitters.
9. A method for remote safe driving of a vehicle, comprising: selecting at least one of a set of vehicular driving actions; and transmitting, over a secure communication channel, the at least one of the set of vehicular driving actions to the vehicle, wherein the set of vehicular driving actions is generated based on a classification of driver behavior.
10. The method of solution 9, wherein the set of vehicular driving actions comprises one or more of parking the vehicle, moving from a predetermined origin to a predetermined destination, and moving to a refueling location.
11. The method of solution 9 or 10, wherein the classification of the plurality of driver behavior is based on a clustering algorithm.
12. The method of any of solutions 9 to 11, wherein the secure communication channel is an operational channel that is secured using an Internet layer cryptographic protocol.
13. The method of any of solutions 9 to 11, wherein the secure communication channel is an emergency channel that is secured using a low-latency Application layer cryptographic protocol.
Implementations of the subject matter and the functional operations described in this patent document can be implemented in various systems, digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Implementations of the subject matter described in this specification can be implemented as one or more computer program products, e.g., one or more modules of computer program instructions encoded on a tangible and non-transitory computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them. The term “data processing unit” or “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.
A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
The processes and logic flows described in this specification can be performed by one or more programmable processors 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, and apparatus can also be implemented as, 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 nonvolatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
It is intended that the specification, together with the drawings, be considered exemplary only, where exemplary means an example. As used herein, “or” is intended to include “and/or”, unless the context clearly indicates otherwise.
While this patent document contains many specifics, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments of particular inventions. Certain features that are described in this patent document in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Moreover, the separation of various system components in the embodiments described in this patent document should not be understood as requiring such separation in all embodiments.
Only a few implementations and examples are described and other implementations, enhancements and variations can be made based on what is described and illustrated in this patent document.
This patent document claims priority to and benefits of U.S. Provisional Patent Application No. 62/730,912, entitled “REMOTE SAFE DRIVING METHODS AND SYSTEMS,” and filed on Sep. 13, 2018. The entire contents of the aforementioned patent application are incorporated by reference as part of the disclosure of this patent document.
Number | Name | Date | Kind |
---|---|---|---|
6084870 | Wooten et al. | Jul 2000 | A |
6263088 | Crabtree | Jul 2001 | B1 |
6594821 | Banning et al. | Jul 2003 | B1 |
6777904 | Degner | Aug 2004 | B1 |
6975923 | Spriggs | Dec 2005 | B2 |
7103460 | Breed | Sep 2006 | B1 |
7499804 | Svendsen | Mar 2009 | B2 |
7689559 | Canright | Mar 2010 | B2 |
7742841 | Sakai et al. | Jun 2010 | B2 |
7783403 | Breed | Aug 2010 | B2 |
7844595 | Canright | Nov 2010 | B2 |
8041111 | Wilensky | Oct 2011 | B1 |
8064643 | Stein | Nov 2011 | B2 |
8082101 | Stein | Dec 2011 | B2 |
8164628 | Stein | Apr 2012 | B2 |
8175376 | Marchesotti | May 2012 | B2 |
8271871 | Marchesotti | Sep 2012 | B2 |
8346480 | Trepagnier et al. | Jan 2013 | B2 |
8378851 | Stein | Feb 2013 | B2 |
8392117 | Dolgov | Mar 2013 | B2 |
8401292 | Park | Mar 2013 | B2 |
8412449 | Trepagnier | Apr 2013 | B2 |
8478072 | Aisaka | Jul 2013 | B2 |
8553088 | Stein | Oct 2013 | B2 |
8706394 | Trepagnier et al. | Apr 2014 | B2 |
8718861 | Montemerlo et al. | May 2014 | B1 |
8788134 | Litkouhi | Jul 2014 | B1 |
8908041 | Stein | Dec 2014 | B2 |
8917169 | Schofield | Dec 2014 | B2 |
8963913 | Baek | Feb 2015 | B2 |
8965621 | Urmson | Feb 2015 | B1 |
8981966 | Stein | Mar 2015 | B2 |
8983708 | Choe et al. | Mar 2015 | B2 |
8993951 | Schofield | Mar 2015 | B2 |
9002632 | Emigh | Apr 2015 | B1 |
9008369 | Schofield | Apr 2015 | B2 |
9025880 | Perazzi | May 2015 | B2 |
9042648 | Wang | May 2015 | B2 |
9081385 | Ferguson et al. | Jul 2015 | B1 |
9088744 | Grauer et al. | Jul 2015 | B2 |
9111444 | Kaganovich | Aug 2015 | B2 |
9117133 | Barnes | Aug 2015 | B2 |
9118816 | Stein | Aug 2015 | B2 |
9120485 | Dolgov | Sep 2015 | B1 |
9122954 | Srebnik | Sep 2015 | B2 |
9134402 | Sebastian | Sep 2015 | B2 |
9145116 | Clarke | Sep 2015 | B2 |
9147255 | Zhang | Sep 2015 | B1 |
9156473 | Clarke | Oct 2015 | B2 |
9176006 | Stein | Nov 2015 | B2 |
9179072 | Stein | Nov 2015 | B2 |
9183447 | Gdalyahu | Nov 2015 | B1 |
9185360 | Stein | Nov 2015 | B2 |
9191634 | Schofield | Nov 2015 | B2 |
9214084 | Grauer et al. | Dec 2015 | B2 |
9219873 | Grauer et al. | Dec 2015 | B2 |
9233659 | Rosenbaum | Jan 2016 | B2 |
9233688 | Clarke | Jan 2016 | B2 |
9248832 | Huberman | Feb 2016 | B2 |
9248835 | Tanzmeister | Feb 2016 | B2 |
9251708 | Rosenbaum | Feb 2016 | B2 |
9277132 | Berberian | Mar 2016 | B2 |
9280711 | Stein | Mar 2016 | B2 |
9282144 | Tebay et al. | Mar 2016 | B2 |
9286522 | Stein | Mar 2016 | B2 |
9297641 | Stein | Mar 2016 | B2 |
9299004 | Lin | Mar 2016 | B2 |
9315192 | Zhu | Apr 2016 | B1 |
9317033 | Ibanez-guzman et al. | Apr 2016 | B2 |
9317776 | Honda | Apr 2016 | B1 |
9330334 | Lin | May 2016 | B2 |
9342074 | Dolgov | May 2016 | B2 |
9347779 | Lynch | May 2016 | B1 |
9355635 | Gao | May 2016 | B2 |
9365214 | Ben Shalom | Jun 2016 | B2 |
9399397 | Mizutani | Jul 2016 | B2 |
9418549 | Kang et al. | Aug 2016 | B2 |
9428192 | Schofield | Aug 2016 | B2 |
9436880 | Bos | Sep 2016 | B2 |
9438878 | Niebla | Sep 2016 | B2 |
9443163 | Springer | Sep 2016 | B2 |
9446765 | Ben Shalom | Sep 2016 | B2 |
9459515 | Stein | Oct 2016 | B2 |
9466006 | Duan | Oct 2016 | B2 |
9476970 | Fairfield | Oct 2016 | B1 |
9483839 | Kwon | Nov 2016 | B1 |
9490064 | Hirosawa | Nov 2016 | B2 |
9494935 | Okumura et al. | Nov 2016 | B2 |
9507346 | Levinson et al. | Nov 2016 | B1 |
9513634 | Pack et al. | Dec 2016 | B2 |
9531966 | Stein | Dec 2016 | B2 |
9535423 | Debreczeni | Jan 2017 | B1 |
9538113 | Grauer et al. | Jan 2017 | B2 |
9547985 | Tuukkanen | Jan 2017 | B2 |
9549158 | Grauer et al. | Jan 2017 | B2 |
9555803 | Pawlicki | Jan 2017 | B2 |
9568915 | Berntorp | Feb 2017 | B1 |
9587952 | Slusar | Mar 2017 | B1 |
9599712 | Van Der Tempel et al. | Mar 2017 | B2 |
9600889 | Boisson et al. | Mar 2017 | B2 |
9602807 | Crane et al. | Mar 2017 | B2 |
9612123 | Levinson et al. | Apr 2017 | B1 |
9620010 | Grauer et al. | Apr 2017 | B2 |
9625569 | Lange | Apr 2017 | B2 |
9628565 | Stenneth et al. | Apr 2017 | B2 |
9649999 | Amireddy et al. | May 2017 | B1 |
9652860 | Maali | May 2017 | B1 |
9669827 | Ferguson et al. | Jun 2017 | B1 |
9672446 | Vallesi-Gonzalez | Jun 2017 | B1 |
9690290 | Prokhorov | Jun 2017 | B2 |
9701023 | Zhang et al. | Jul 2017 | B2 |
9712754 | Grauer et al. | Jul 2017 | B2 |
9720418 | Stenneth | Aug 2017 | B2 |
9723097 | Harris | Aug 2017 | B2 |
9723099 | Chen | Aug 2017 | B2 |
9723233 | Grauer et al. | Aug 2017 | B2 |
9726754 | Massanell et al. | Aug 2017 | B2 |
9729860 | Cohen et al. | Aug 2017 | B2 |
9738280 | Rayes | Aug 2017 | B2 |
9739609 | Lewis | Aug 2017 | B1 |
9746550 | Nath | Aug 2017 | B2 |
9753128 | Schweizer et al. | Sep 2017 | B2 |
9753141 | Grauer et al. | Sep 2017 | B2 |
9754490 | Kentley et al. | Sep 2017 | B2 |
9760837 | Nowozin et al. | Sep 2017 | B1 |
9766625 | Boroditsky et al. | Sep 2017 | B2 |
9769456 | You et al. | Sep 2017 | B2 |
9773155 | Shotton et al. | Sep 2017 | B2 |
9779276 | Todeschini et al. | Oct 2017 | B2 |
9785149 | Wang et al. | Oct 2017 | B2 |
9805294 | Liu et al. | Oct 2017 | B2 |
9810785 | Grauer et al. | Nov 2017 | B2 |
9823339 | Cohen | Nov 2017 | B2 |
9953236 | Huang | Apr 2018 | B1 |
10147193 | Huang | Dec 2018 | B2 |
10223806 | Yi et al. | Mar 2019 | B1 |
10223807 | Yi et al. | Mar 2019 | B1 |
10410055 | Wang et al. | Sep 2019 | B2 |
10676099 | Muehlmann | Jun 2020 | B2 |
10782685 | Sucan | Sep 2020 | B1 |
10919463 | Brown | Feb 2021 | B1 |
11022977 | Ho | Jun 2021 | B2 |
20030114980 | Klausner et al. | Jun 2003 | A1 |
20030174773 | Comaniciu | Sep 2003 | A1 |
20040264763 | Mas et al. | Dec 2004 | A1 |
20070183661 | El-Maleh | Aug 2007 | A1 |
20070183662 | Wang | Aug 2007 | A1 |
20070230792 | Shashua | Oct 2007 | A1 |
20070286526 | Abousleman | Dec 2007 | A1 |
20080249667 | Horvitz | Oct 2008 | A1 |
20090040054 | Wang | Feb 2009 | A1 |
20090087029 | Coleman | Apr 2009 | A1 |
20100049397 | Lin | Feb 2010 | A1 |
20100111417 | Ward | May 2010 | A1 |
20100226564 | Marchesotti | Sep 2010 | A1 |
20100281361 | Marchesotti | Nov 2010 | A1 |
20110142283 | Huang | Jun 2011 | A1 |
20110206282 | Aisaka | Aug 2011 | A1 |
20110247031 | Jacoby | Oct 2011 | A1 |
20120041636 | Johnson et al. | Feb 2012 | A1 |
20120105639 | Stein | May 2012 | A1 |
20120140076 | Rosenbaum | Jun 2012 | A1 |
20120274629 | Baek | Nov 2012 | A1 |
20120314070 | Zhang et al. | Dec 2012 | A1 |
20130051613 | Bobbitt et al. | Feb 2013 | A1 |
20130083959 | Owechko | Apr 2013 | A1 |
20130182134 | Grundmann et al. | Jul 2013 | A1 |
20130204465 | Phillips et al. | Aug 2013 | A1 |
20130266187 | Bulan | Oct 2013 | A1 |
20130329052 | Chew | Dec 2013 | A1 |
20140072170 | Zhang | Mar 2014 | A1 |
20140104051 | Breed | Apr 2014 | A1 |
20140142799 | Ferguson et al. | May 2014 | A1 |
20140143839 | Ricci | May 2014 | A1 |
20140145516 | Hirosawa | May 2014 | A1 |
20140195214 | Kozloski et al. | Jul 2014 | A1 |
20140198184 | Stein | Jul 2014 | A1 |
20140321704 | Partis | Oct 2014 | A1 |
20140334668 | Saund | Nov 2014 | A1 |
20150062304 | Stein | Mar 2015 | A1 |
20150269438 | Samarsekera et al. | Sep 2015 | A1 |
20150310370 | Burry | Oct 2015 | A1 |
20150353082 | Lee et al. | Dec 2015 | A1 |
20160008988 | Kennedy | Jan 2016 | A1 |
20160026787 | Nairn et al. | Jan 2016 | A1 |
20160037064 | Stein | Feb 2016 | A1 |
20160094774 | Li | Mar 2016 | A1 |
20160118080 | Chen | Apr 2016 | A1 |
20160129907 | Kim | May 2016 | A1 |
20160165157 | Stein | Jun 2016 | A1 |
20160210528 | Duan | Jul 2016 | A1 |
20160275766 | Venetianer et al. | Sep 2016 | A1 |
20160321381 | English | Nov 2016 | A1 |
20160334230 | Ross et al. | Nov 2016 | A1 |
20160342837 | Hong et al. | Nov 2016 | A1 |
20160347322 | Clarke et al. | Dec 2016 | A1 |
20160375907 | Erban | Dec 2016 | A1 |
20170053169 | Cuban et al. | Feb 2017 | A1 |
20170061632 | Linder et al. | Mar 2017 | A1 |
20170090476 | Letwin | Mar 2017 | A1 |
20170124476 | Levinson et al. | May 2017 | A1 |
20170134631 | Zhao et al. | May 2017 | A1 |
20170177951 | Yang et al. | Jun 2017 | A1 |
20170301104 | Qian | Oct 2017 | A1 |
20170305423 | Green | Oct 2017 | A1 |
20170318407 | Meister | Nov 2017 | A1 |
20170341575 | Hauler | Nov 2017 | A1 |
20180024552 | She | Jan 2018 | A1 |
20180050704 | Tascione | Feb 2018 | A1 |
20180074490 | Park | Mar 2018 | A1 |
20180088589 | Hasegawa | Mar 2018 | A1 |
20180151063 | Pun | May 2018 | A1 |
20180154906 | Dudar | Jun 2018 | A1 |
20180158197 | Dasgupta | Jun 2018 | A1 |
20180260956 | Huang | Sep 2018 | A1 |
20180283892 | Behrendt | Oct 2018 | A1 |
20180364705 | Yunoki | Dec 2018 | A1 |
20180373980 | Huval | Dec 2018 | A1 |
20190001989 | Schoenfeld | Jan 2019 | A1 |
20190025853 | Julian | Jan 2019 | A1 |
20190064801 | Frazzoli | Feb 2019 | A1 |
20190065863 | Luo et al. | Feb 2019 | A1 |
20190066329 | Yi et al. | Feb 2019 | A1 |
20190066330 | Yi et al. | Feb 2019 | A1 |
20190066344 | Yi et al. | Feb 2019 | A1 |
20190108384 | Wang et al. | Apr 2019 | A1 |
20190132391 | Thomas | May 2019 | A1 |
20190132392 | Liu | May 2019 | A1 |
20190171205 | Kudanowski | Jun 2019 | A1 |
20190196514 | Kanehara | Jun 2019 | A1 |
20190210564 | Han | Jul 2019 | A1 |
20190210613 | Sun | Jul 2019 | A1 |
20190236950 | Li | Aug 2019 | A1 |
20190250620 | Huang | Aug 2019 | A1 |
20190266420 | Ge | Aug 2019 | A1 |
20190337526 | Rave | Nov 2019 | A1 |
20200031362 | Lee | Jan 2020 | A1 |
20200139990 | Hiruma | May 2020 | A1 |
20200331484 | Rodriguez Bravo | Oct 2020 | A1 |
20210089018 | Nordbruch | Mar 2021 | A1 |
20210116907 | Altman | Apr 2021 | A1 |
20210316742 | Hayes | Oct 2021 | A1 |
20210331686 | Beyers | Oct 2021 | A1 |
Number | Date | Country |
---|---|---|
106340197 | Jan 2017 | CN |
106354130 | Jan 2017 | CN |
106781591 | May 2017 | CN |
108010360 | May 2018 | CN |
2608513 | Sep 1977 | DE |
890470 | Jan 1999 | EP |
2448251 | May 2012 | EP |
2463843 | Jun 2012 | EP |
2993654 | Mar 2016 | EP |
3081419 | Oct 2016 | EP |
2006-293837 | Oct 2006 | JP |
2015074322 | Apr 2015 | JP |
10-2002-0092593 | Dec 2002 | KR |
100802511 | Feb 2008 | KR |
1991009375 | Jun 1991 | WO |
2005098739 | Oct 2005 | WO |
2005098751 | Oct 2005 | WO |
2005098782 | Oct 2005 | WO |
2010109419 | Sep 2010 | WO |
2013045612 | Apr 2013 | WO |
2014111814 | Jul 2014 | WO |
2014166245 | Oct 2014 | WO |
2014201324 | Dec 2014 | WO |
2015083009 | Jun 2015 | WO |
2015103159 | Jul 2015 | WO |
2015125022 | Aug 2015 | WO |
2015186002 | Dec 2015 | WO |
2016090282 | Jun 2016 | WO |
2016135736 | Sep 2016 | WO |
2017079349 | May 2017 | WO |
2017079460 | May 2017 | WO |
2017013875 | May 2018 | WO |
2019040800 | Feb 2019 | WO |
2019084491 | May 2019 | WO |
2019084494 | May 2019 | WO |
2019140277 | Jul 2019 | WO |
2019168986 | Sep 2019 | WO |
WO 2020058375 | Mar 2020 | WO |
Entry |
---|
Carle, Patrick J.F. et al. “Global Rover Localization by Matching Lidar and Orbital 3D Maps.” IEEE, Anchorage Convention District, pp. 1-6, May 3-8, 2010. (Anchorage Alaska, US). |
Caselitz, T. et al., “Monocular camera localization in 3D LiDAR maps,” European Conference on Computer Vision (2014) Computer Vision—ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol. 8690, pp. 1-6, Springer, Cham. |
Mur-Artal, R. et al., “ORB-SLAM: A Versatile and Accurate Monocular SLAM System,” IEEE Transaction on Robotics, Oct. 2015, pp. 1147-1163, vol. 31, No. 5, Spain. |
Sattler, T. et al., “Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?” CVPR, IEEE, 2017, pp. 1-10. |
Engel, J. et la. “LSD-SLAM: Large Scare Direct Monocular SLAM,” pp. 1-16, Munich. |
Levinson, Jesse et al., Experimental Robotics, Unsupervised Calibration for Multi-Beam Lasers, pp. 179-194, 12th Ed., Oussama Khatib, Vijay Kumar, Gaurav Sukhatme (Eds.) Springer-Verlag Berlin Heidelberg 2014. |
Geiger, Andreas et al., “Automatic Camera and Range Sensor Calibration using a single Shot”, Robotics and Automation (ICRA), pp. 1-8, 2012 IEEE International Conference. |
Zhang, Z. et al. A Flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence (vol. 22, Issue: 11, pp. 1-5, Nov. 2000). |
Bar-Hillel, Aharon et al. “Recent progress in road and lane detection: a survey.” Machine Vision and Applications 25 (2011), pp. 727-745. |
Schindler, Andreas et al. “Generation of high precision digital maps using circular arc splines,” 2012 IEEE Intelligent Vehicles Symposium, Alcala de Henares, 2012, pp. 246-251. doi: 10.1109/IVS.2012.6232124. |
Hou, Xiaodi et al., “Saliency Detection: A Spectral Residual Approach”, Computer Vision and Pattern Recognition, CVPR'07—IEEE Conference, pp. 1-8, 2007. |
Hou, Xiaodi et al., “Image Signature: Highlighting Sparse Salient Regions”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, No. 1, pp. 194-201, 2012. |
Hou, Xiaodi et al., “Dynamic Visual Attention: Searching For Coding Length Increments”, Advances in Neural Information Processing Systems, vol. 21, pp. 681-688, 2008. |
Li, Yin et al., “The Secrets of Salient Object Segmentation”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 280-287, 2014. |
Zhou, Bolei et al., “A Phase Discrepancy Analysis of Object Motion”, Asian Conference on Computer Vision, pp. 225-238 (pp. 1-14), Springer Berlin Heidelberg, 2010. |
Hou, Xiaodi and Yuille, Alan and Koch, Christof, “Boundary Detection Benchmarking Beyond F-Measures”, Computer Vision and Pattern Recognition, CVPR'13, vol. 2013, pp. 1-8, IEEE, 2013. |
Hou, Xiaodi et al., “Color Conceptualization”, Proceedings of the 15th ACM International Conference on Multimedia, pp. 265-268, ACM, 2007. |
Hou, Xiaodi, et al., “Thumbnail Generation Based on Global Saliency”, Advances in Cognitive Neurodynamics, ICCN 2007, pp. 999-1003, Springer Netherlands, 2008. |
Hou, Xiaodi et al., “A Meta-Theory of Boundary Detection Benchmarks”, arXiv preprint arXiv: 1302.5985, pp. 1-4, 2013. |
Li, Yanghao et al., “Revisiting Batch Normalization for Practical Domain Adaptation”, arXiv preprint arXiv: 1603.04779, pp. 1-12, 2016. |
Hou, Xiaodi, et al., “A Time-Dependent Model of Information Capacity of Visual Attention”, International Conference on Neural Information Processing, pp. 127-136, Springer Berlin Heidelberg, 2006. |
Wang, Panqu and Chen, Pengfei and Yuan, Ye and Liu, Ding and Huang, Zehua and Hou, Xiaodi and Cottrell, Garrison, “Understanding Convolution for Semantic Segmentation”, arXiv preprint arXiv: 1702.08502, pp. 1-10, 2017. |
Li, Yanghao and Wang, Naiyan and Liu, Jiaying and Hou, Xiaodi, “Factorized Bilinear Models for Image Recognition”, arXiv preprint arXiv: 1611.05709, pp. 1-9, 2016. |
Hou, Xiaodi, “Computational Modeling and Psychophysics in Low and Mid-Level Vision”, California Institute of Technology, pp. 1-125, 2014. |
Spinello, Luciano, Triebel, Rudolph, Siegwart, Roland, “Multiclass Multimodal Detection and Tracking in Urban Environments”, Sage Journals, vol. 29 Issue 12, pp. 1498-1515 (pp. 18), Article first published online: Oct. 7, 2010; Issue published: Oct. 1, 2010. |
Barth, Matthew et al., “Recent Validation Efforts for a Comprehensive Modal Emissions Model”, Transportation Research Record 1750, Paper No. 01-0326, College of Engineering, Center for Environmental Research and Technology, University of California, Riverside, CA 92521, pp. 1-11, date unknown. |
Ramos, Sebastian, et al., “Detecting Unexpected Obstacles for Self-Driving Cars Fusing Deep Learning and Geometric Modeling”, arXiv:1612.06573vl [cs.CV] pp. 1-8, Dec. 20, 2016. |
Schroff, Florian, Dmitry Kalenichenko, James Philbin, (Google), “FaceNet: A Unified Embedding for Face Recognition and Clustering”, CVPR, pp. 1-10, 2015. |
Dai, Jifeng, Kaiming He, Jian Sun, (Microsoft Research), “Instance-aware Semantic Segmentation via Multi-task Network Cascades”, CVPR, pp. 1, 2016. |
Huval, Brody et al., “An Empirical Evaluation of Deep Learning on Highway Driving”, arXiv:1504.01716v3 [cs.RO] pp. 1-7, Apr. 17, 2015. |
Li, Tian, “Proposal Free Instance Segmentation Based on Instance-aware Metric”, Department of Computer Science, Cranberry-Lemon University, Pittsburgh, PA., pp. 1-2, date unknown. |
Norouzi, Mohammad, et al., “Hamming Distance Metric Learning”, Departments of Computer Science and Statistics, University of Toronto, pp. 1-9, date unknown. |
Jain, Suyong Dutt, Grauman, Kristen, “Active Image Segmentation Propagation”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, pp. 1-10, Jun. 2016. |
MacAodha, Oisin, Campbell, Neill D.F., Kautz, Jan, Brostow, Gabriel J., “Hierarchical Subquery Evaluation for Active Learning on a Graph”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2014. |
Kendall, Alex, Gal, Yarin, “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision”, arXiv:1703.04977v1 [cs.CV] pp. 1-11, Mar. 15, 2017. |
Wei, Junqing, John M. Dolan, Bakhtiar Litkhouhi, “A Prediction- and Cost Function-Based Algorithm for Robust Autonomous Freeway Driving”, 2010 IEEE Intelligent Vehicles Symposium, University of California, San Diego, CA, USA, pp. 1-6, Jun. 21-24, 2010. |
Welinder, Peter, et al., “The Multidimensional Wisdom of Crowds” http://www.vision.caltech.edu/visipedia/papers/WelinderEtaINIPS10.pdf, pp. 1-9, 2010. |
Yu, Kai et al., “Large-scale Distributed Video Parsing and Evaluation Platform”, Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, China, arXiv:1611.09580v1 [cs.CV], pp. 1-7, Nov. 29, 2016. |
P. Guarneri, G. Rocca and M. Gobbi, “A Neural-Network-Based Model for the Dynamic Simulation of the Tire/Suspension System While Traversing Road Irregularities,” in IEEE Transactions on Neural Networks, vol. 19, No. 9, pp. 1549-1563, Sep. 2008. |
C. Yang, Z. Li, R. Cui and B. Xu, “Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 25, No. 11, pp. 2004-2016, Nov. 2014. |
Richter, Stephan R. et al., “Playing for Data: Ground Truth from Computer Games”, Intel Labs, European Conference on Computer Vision (ECCV), Amsterdam, the Netherlands, pp. 1-16, 2016. |
Athanasiadis, Thanos, et al., “Semantic Image Segmentation and Object Labeling”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, No. 3, pp. 1-15, Mar. 2007. |
Cordts, Marius et al., “The Cityscapes Dataset for Semantic Urban Scene Understanding”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, pp. 1-29, 2016. |
Somani, Adhira et al., “DESPOT: Online POMDP Planning with Regularization”, Department of Computer Science, National University of Singapore, pp. 1-9, date unknown. |
Paszke, Adam et al., Enet: A deep neural network architecture for real-time semantic segmentation. CoRR, abs/1606.02147, pp. 1-10, 2016. |
Szeliski, Richard, “Computer Vision: Algorithms and Applications” http://szeliski.org/Book/, pp. 1-2, 2010. |
Ahn, Kyoungho, Hesham Rakha, “The Effects of Route Choice Decisions on Vehicle Energy Consumption and Emissions”, Virginia Tech Transportation Institute, Blacksburg, VA 24061, pp. 1-34, date unknown. |
Park, Tae Wook. International Application No. PCT/US2019/050908, International Search Report and Written Opinion dated Jan. 6, 2020 (pp. 1-14). |
International Preliminary Report on Patentability dated Mar. 25, 2021 for International Patent Application No. PCT/US2019/050908 (10 pages). |
Number | Date | Country | |
---|---|---|---|
20200086884 A1 | Mar 2020 | US |
Number | Date | Country | |
---|---|---|---|
62730912 | Sep 2018 | US |