IBM
Armonk, NY, United States
Armonk, NY, United States

The International Business Machines Corporation is an American multinational technology and consulting corporation, with headquarters in Armonk, New York, United States. IBM manufactures and markets computer hardware and software, and offers infrastructure, hosting and consulting services in areas ranging from mainframe computers to nanotechnology.The company was founded in 1911 as the Computing-Tabulating-Recording Company through a merger of the Tabulating Machine Company, the International Time Recording Company, and the Computing Scale Company. CTR was changed to "International Business Machines" in 1924, using a name which had originated with CTR's Canadian subsidiary. The acronym IBM followed. Securities analysts nicknamed the company Big Blue for its size and common use of the color in products, packaging, and logo.In 2012, Fortune ranked IBM the No. 2 largest U.S. firm in terms of number of employees , the No. 4 largest in terms of market capitalization, the No. 9 most profitable, and the No. 19 largest firm in terms of revenue. Globally, the company was ranked the No. 31 largest in terms of revenue by Forbes for 2011. Other rankings for 2011/2012 include No. 1 company for leaders , No. 1 green company in the U.S. , No. 2 best global brand , No. 2 most respected company , No. 5 most admired company , and No. 18 most innovative company .IBM has 12 research laboratories worldwide, bundled into IBM Research. As of 2013 the company held the record for most patents generated by a business for 22 consecutive years. Its employees have garnered five Nobel Prizes, six Turing Awards, ten National Medals of Technology, and five National Medals of Science. Notable company inventions include the automated teller machine , the floppy disk, the hard disk drive, the magnetic stripe card, the relational database, the Universal Product Code , the financial swap, the Fortran programming language, SABRE airline reservation system, DRAM, copper wiring in semiconductors, the silicon-on-insulator semiconductor manufacturing process, and Watson artificial intelligence.IBM has constantly evolved since its inception, acquiring properties such as Kenexa and SPSS and organizations such as PwC's consulting business , spinning off companies like printer manufacturer Lexmark , and selling off product lines like its personal computer and server businesses to Lenovo . In 2014 IBM announced that it would "offload" IBM Micro Electronics semiconductor manufacturing to Global Foundries. This transition is in progress as of early 2015. Wikipedia.

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The Following Companies As The Key Players In The Global Connected Car M2m Market: Alpine Electronics, BMW, GM, and Audi. Other Prominent Vendors in the market are: Bosch, Delphi Automotive, Ford Motor Company, Google, IBM, Mercedes-Benz, NXP Semiconductors, PSA Peugeot Citroën, Qualcomm, Sierra Wireless, Tech Mahindra, To yota, Volkswagen, and Wipro. Commenting on the report, “One trend in market is big data platform provided by connected cars to foster further developments. The growth of connectivity solutions in the automotive segment provides a huge opportunity to the automobile OEMs to develop an efficient product for the customers using real-time data. This data is expected to grow further due to the increasing adoption of connected cars during the forecast period. For instance, telematic devices will produce data for information like date, time, speed, acceleration, deceleration, cumulative mileage, fuel consumption, and navigation details. This data is approximately in the range of 6 MB to 20 MB per customer annually. Hence, the total data will be more than 1 TB per year for 100,000 vehicles. Such crucial data can be used by many stakeholders to augment their revenue stream.” The report covers the present scenario and the growth prospects of the global connected car M2M market for 2017-2021. To calculate the market size, the report considers the revenue generated from OEM and aftermarket equipment. The market is divided into the following segments based on geography: According to the report, one driver in market is availability of high-speed wireless networks. Telecom operators play a significant role in the growth of the global connected car M2M market as the connectivity of the connected car M2M modules and platforms depend solely on telecom networks. Some connected car M2M services require high-speed wireless networks. For instance, on-the-move radio streaming, music, video, location-based services (LBS), and on-demand content require high-speed wireless networks. Due to the wide reach of 3G and LTE networks, telecom operators can provide high-speed connectivity at lower tariffs to connected car M2M users. Many car manufacturers, connected car M2M platform developers, and telecom operators are expected to develop partnerships to launch LTE-based connected cars during the forecast period. The market has witnessed similar strategic partnerships in the past that fostered market growth. For instance, in 2014, Audi announced the launch of its S3 Sportback model in Europe with an embedded Gemalto LTE module. Further, the report states that one challenge in market is lack of convergence between the lifecycles of automotive and mobile industries. The mobile devices industry has faster product lifecycles than the automotive industry. The lifecycle of products in the automotive industry is lengthier by quite a few years when compared to the smartphone industry. For example, the lifespan of a notebook or a laptop is about two and a half years, whereas a car is scrapped on an average after 15 years. This difference would prevent customers from following-up on the updates for hardware, new technology, and software that are evolving on a continuous basis in the connected cars segment. Infotainment systems that are generally embedded in the vehicle offer entertainment, phone, and navigation services via a touchscreen or display unit. However, the consumers are feeling disconnected with their IVI systems owing to the lag between the product cycles of smartphones and automobiles in the past few years. The technological advances in the vehicles take years to get commercialized. Thus, the automotive manufacturers and automotive infotainment manufacturers are not able to keep pace with the latest smartphones updates. The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to a SWOT analysis of the key vendors. For more information, please visit http://www.orbisresearch.com/reports/index/global-connected-car-m2m-market-2017-2021


A system (and method) includes at least one traffic server to receive traffic information reports transmitted from vehicles located on a traffic network serviced by the at least one traffic server. The traffic network is segmented into a plurality of predefined links, each predefined link associated with a priority level, a reporting latency, and an identification code. An onboard unit in a vehicle transmits traffic information reports to the at least one traffic server. The onboard unit is configured: to determine a current location of the vehicle; based on the current location, to determine on which of a link of the predefined links in the traffic network the vehicle is currently located; to determine a link identification code that corresponds to the link on which the vehicle is currently located; and to generate a traffic data packet to be potentially transmitted by the onboard unit to the at least one traffic server. The determined link identification code serves as indicia of location in the traffic data packet.

Claims which contain your search:

1. A system, comprising: at least one traffic server to receive traffic information reports transmitted from vehicles located on a traffic network serviced by said at least one traffic server, said traffic network being segmented into a plurality of predefined links, each predefined link associated with a priority level, a reporting latency, and an identification code; and an onboard unit in a vehicle to transmit traffic information reports to said at least one traffic server, said onboard unit configured:to determine a current location of said vehicle;based on said current location, to determine on which of a link of said predefined links in said traffic network said vehicle is currently located;to determine a link identification code that corresponds to said link on which said vehicle is currently located; andto generate a traffic data packet to be potentially transmitted by said onboard unit to said at least one traffic server, wherein said determined link identification code serves as an indicia of location in said traffic data packet.

2. The system according to claim 1, wherein each link of said plurality of predefined links is defined as a set of four GPS (Global Positioning System) coordinates, and wherein said onboard unit uses a geo-fencing calculation based on said four GPS coordinates to determine on which link said vehicle is currently located.

3. The system according to claim 1, wherein said traffic data packet further includes an entry time into said link and an exit time from said link.

4. The system according to claim 3, wherein said onboard unit is further configured: to determine a potential transmission time for said traffic data packet to be transmitted to the at least one traffic server, as based on said latency of said identified link; to determine a probability level for the traffic data packet, as based on said priority level of said identified link; and to store said traffic data packet in a transmission queue, wherein traffic data packets are stored in said transmission queue in an ordering of potential transmission times for each stored traffic data packet.

5. The system of claim 4, wherein said onboard unit is further configured: when a current time matches a potential transmission time for one of said traffic data packets stored in said transmission queue, to generate an output from a random number generator having parameters set for a priority level of the identified link associated with the traffic data packet with the potential transmission time; to compare said random number generator output with said probability level for said traffic data packet; and, based on said comparing, either to drop said data packet from said transmission queue or to make a transmission to said at least one traffic server of said traffic data packet along with other traffic data packets stored in said transmission queue up to a transmission length not to exceed a predetermined length based on a quantum charge for transmissions.

6. The system of claim 5, wherein said onboard unit is configured with a vehicle-to-vehicle (V2V) system and wherein said onboard unit, prior to making a transmission report to said at least one traffic server, communicates using said V2V system to aggregate data for said transmission report with data from one or more other vehicles.

7. A method, comprising: in an onboard unit installed in a vehicle, determining that said vehicle has entered onto a link of a plurality of network traffic links; determining an identification of said link; determining an entry time and an exit time of said vehicle for said determined link; generating a traffic data report packet including an identification of said link and said entry and exit times; assigning to said traffic data report packet:a potential transmission time for potentially making a transmission of said traffic data report packet; anda probability value assigned to said traffic data report packet as based on a priority of said identified link; and storing said generated traffic data report in a transmission queue along with said assigned potential transmission time and said probability value, wherein said onboard unit stores and maintains said generated traffic data report packet in said transmission queue with other traffic data report packets in a sequence in accordance with potential transmission times of said other traffic data report packets.

8. The method according to claim 7, further comprising: whenever a current time matches a potential transmission time for any of said traffic data report packets stored in said transmission queue, said onboard unit generates an output value from a random number generator having parameters set in accordance with a priority value of a traffic link of said traffic data report packet with said potential transmission time; and based on a comparison of said generated output value with the probability value assigned to said traffic data report packet, said onboard unit executes one of:drops said traffic data report packet from said transmission queue; andmakes a transmission to a traffic center server via a communication link of said traffic data report packet, said transmission including other traffic data report packets in said transmission queue, up to a preset transmission length based on a quantum cost of transmission via said communication link.

9. The method according to claim 8, wherein said onboard unit has a vehicle-to-vehicle (V2V) communication system capability to communicate with onboard units of other vehicles, said method further comprising using said V2V system to aggregate traffic data report packets from at least one other vehicle in said transmission to said traffic center server.

10. The method of claim 7, wherein said potential transmission time is based on a latency assigned to said identified link, based on a priority of said identified link in said traffic network.

11. The method of claim 7, wherein said onboard unit is GPS-capable, wherein each link of said plurality of network traffic links is defined as a set of four global positioning system (GPS) coordinates, and wherein said onboard unit uses a geo-fencing calculation to determine on which link said vehicle is currently located.

15. An apparatus, comprising: a processor; a transceiver; and at least one non-transitory memory device, wherein at least one said non-transitory memory device stores a set of instructions to permit said apparatus to function as an onboard unit on a vehicle by:determining a current location of a vehicle in which said onboard unit is installed;determining, using said current location, that said vehicle has entered onto a specific network link of a plurality of previously-defined network links of a traffic network and identifying a link identification code corresponding to said specific network link;identifying a time of entry into said specific network link and a time of exit from said specific network link;generating a traffic data report packet including said link identification code and said entry and exit times;assigning a potential transmission time and a probability value to said traffic data report packet, said potential transmission time and said probability value based on a predefined priority of said specific network link in said traffic network; andstoring, in a transmission queue, said traffic data report packet along with said assigned potential transmission time and said assigned probability value.

16. The apparatus of claim 15, wherein said traffic data report packet is stored and maintained in said transmission queue with other traffic data report packets in a sequence according to potential transmission times.

17. The apparatus of claim 16, wherein, whenever a current time matches a potential transmission time of any of said traffic data report packets stored in said transmission queue, said onboard unit generates an output value from a random number generator having parameters set in accordance with a priority value of a traffic link of said traffic data report packet with said potential transmission time; and based on a comparison of said generated output value with the probability value assigned to said traffic data report packet, said onboard unit executes one of:drops said traffic data report packet from said transmission queue; andmakes a transmission to a traffic center server via a communication link of said traffic data report packet, said transmission including other traffic data report packets in said transmission queue, up to a preset transmission length based on a quantum cost of transmission via said communication link.

18. The apparatus of claim 17, wherein said onboard unit has a vehicle-to-vehicle (V2V) communication system capability to communicate with onboard units of other vehicles, said method further comprising using said V2V system to aggregate traffic data report packets from at least one other vehicle in said transmission to said traffic center server.

19. The apparatus of claim 15, further comprising a Global Positioning System (GPS) circuit, and wherein each link of said plurality of previously-defined network links of set traffic network is defined as a set of four GPS coordinates, and wherein said determining that said vehicle has entered into a specific network link comprises a geo-fencing method using GPS coordinates.


Patent
Ibm | Date: 2015-08-25

Contextualizing vehicle data and predicting real-time driver actions. By unsiloing collected data relating to a driver, the actions of the driver can be predicted and the reasons for variations from the predicted actions can be determined based on the contextualized data.

Claims which contain your search:

1. A method comprising: determining a driver of a vehicle based, at least in part, on a set of data collected from a set of vehicle data sensors; gathering a set of context data from the set of vehicle data sensors and a set of external data sensors; and predicting a set of actions to be taken by the driver based, at least in part, on the set of context data; wherein:the set of vehicle data sensors captures data within the vehicle;the set of external sensors captures data external to the vehicle; andat least determining the driver is performed by computer software running on computer hardware.

3. The method of claim 1, further comprising: determining that the driver did not take at least one action in the predicted set of actions; and taking a preventative action; wherein:a preventative action is one of: communicating with the driver, communicating with a second vehicle, or communicating with a public safety system.

4. The method of claim 1, wherein the set of data collected from a set of vehicle data sensors includes a set of biometric data identifying the driver.

5. The method of claim 1, wherein predicting a set of actions to be taken by the driver occurs in real-time.

6. The method of claim 1, wherein determining the driver of the vehicle includes: comparing the set of data collected from the set of vehicle data sensors to a set of stored driver profiles; and determining a driver profile that corresponds to the driver.

7. The method of claim 6, wherein predicting the set of actions to be taken by the driver includes: analyzing the set of context data; analyzing the driver profile that corresponds to the driver; and determining the set of actions to be taken by the driver, based on the set of context data and the driver profile that corresponds to the driver; wherein:the driver profile that corresponds to the driver includes a set of historical driver data based, at least in part, on a set of previous driving events.

8. A computer program product comprising: a computer readable storage medium having stored thereon:first instructions executable by a device to cause the device to determine a driver of a vehicle based, at least in part, on a set of data collected from a set of vehicle data sensors;second instructions executable by a device to cause the device to gather a set of context data from the set of vehicle data sensors and a set of external data sensors; andthird instructions executable by a device to cause the device to predict a set of actions to be taken by the driver based, at least in part, on the set of context data;wherein:

10. The computer program product of claim 8, further comprising: fourth instructions executable by a device to cause the device to determine that the driver did not take at least one action in the predicted set of actions; and fifth instructions executable by a device to cause the device to take a preventative action; wherein:a preventative action is one of: communicating with the driver, communicating with a second vehicle, or communicating with a public safety system.

11. The computer program product of claim 8, wherein the set of data collected from a set of vehicle data sensors includes a set of biometric data identifying the driver.

12. The computer program product of claim 8, wherein the third instructions to predict a set of actions to be taken by the driver occurs in real-time.

13. The computer program product of claim 8, wherein first instructions to determine the driver of the vehicle include: fourth instructions executable by a device to cause the device to compare the set of data collected from the set of vehicle data sensors to a set of stored driver profiles; and fifth instructions executable by a device to cause the device to determine a driver profile that corresponds to the driver.

14. A computer system comprising: a processor set; and a computer readable storage medium; wherein:the processor set is structured, located, connected, and/or programmed to run instructions stored on the computer readable storage medium; andthe instructions include:

16. The computer system of claim 14, further comprising: fourth instructions executable by a device to cause the device to determine that the driver did not take at least one action in the predicted set of actions; and fifth instructions executable by a device to cause the device to take a preventative action; wherein:a preventative action is one of: communicating with the driver, communicating with a second vehicle, or communicating with a public safety system.

17. The computer system of claim 14, wherein the set of data collected from a set of vehicle data sensors includes a set of biometric data identifying the driver.

18. The computer system of claim 14, wherein the third instructions to predict a set of actions to be taken by the driver occurs in real-time.

19. The computer system of claim 14, wherein first instructions to determine the driver of the vehicle include: fourth instructions executable by a device to cause the device to compare the set of data collected from the set of vehicle data sensors to a set of stored driver profiles; and fifth instructions executable by a device to cause the device to determine a driver profile that corresponds to the driver.

20. The computer system of claim 19, wherein predicting the set of actions to be taken by the driver includes: sixth instructions executable by a device to cause the device to analyze the set of context data; seventh instructions executable by a device to cause the device to analyze the driver profile that corresponds to the driver; and eighth instructions executable by a device to cause the device to determine the set of actions to be taken by the driver, based on the set of context data and the driver profile that corresponds to the driver; wherein:the driver profile that corresponds to the driver includes a set of historical driver data based, at least in part, on a set of previous driving events.


Patent
Ibm | Date: 2015-09-23

As disclosed herein a method, executed by a computer, includes monitoring proximate automobiles using a camera, receiving a request to transmit a communication connection request to a selected automobile, and determining observed attributes corresponding to the selected automobile based on images from the camera. The method further includes broadcasting, over a network, the observed attributes to the proximate automobiles, and requesting disclosed attributes and a connection identifier from the proximate automobiles that match the observed attributes, receiving at least one response from the proximate automobiles that match the observed attributes, and determining which response is a best match to the selected automobile. The method further includes transmitting the communication connection request to the selected automobile over the network using the connection identifier corresponding to the best match. A computer program product corresponding to the above method is also disclosed herein.

Claims which contain your search:

1. A method comprising: monitoring proximate automobiles using a camera; receiving a request to transmit a communication connection request to a selected automobile; determining observed attributes corresponding to the selected automobile based on images from the camera; broadcasting, over a network, the observed attributes to the proximate automobiles and requesting disclosed attributes and a connection identifier from proximate automobiles that match the observed attributes; receiving at least one response from the proximate automobiles that match the observed attributes; determining which response is a best match to the selected automobile; and transmitting the communication connection request to the selected automobile over the network using the connection identifier corresponding to the best match.

8. The method of claim 1, further comprising responding to voice commands from a driver of an automobile.

9. A method comprising: receiving, from a broadcasting automobile, a broadcast comprising observed attributes; determining if the observed attributes describe a receiving automobile; responding to the broadcast with disclosed attributes and a connection identifier; receiving, over a network, a communication connection request from the automobile; and accepting the communication connection request.

15. A computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising instructions to: monitor proximate automobiles using a camera; receive a request to transmit a communication connection request to a selected automobile; determine observed attributes corresponding to the selected automobile based on images from the camera; broadcast, over a network, the observed attributes to the proximate automobiles and requesting disclosed attributes and a connection identifier from the proximate automobiles that match the observed attributes; receive at least one response from the proximate automobiles; determine which response is a best match to the selected automobile; and transmit the communication connection request to the selected automobile over the network using the connection identifier corresponding to the best match.

17. The computer program product of claim 15, wherein the program instructions comprise instructions to determine if the selected automobile is still visible on a display.

18. The computer program product of claim 17, wherein the program instructions comprise instructions to label the selected automobile on the display with an identifying tag.

19. The computer program product of claim 15, wherein the program instructions comprise instructions to determine if a selected image is an automobile.

20. The computer program product of claim 15, wherein the program instructions comprise instructions to respond to voice commands from a driver of an automobile.


FRANKFURT, Germany--(BUSINESS WIRE)--HARMAN International, a wholly-owned subsidiary of Samsung Electronics Co., Ltd. focused on connected technologies for automotive, consumer and enterprise markets, today announced its collaboration with IBM, G+D Mobile Security and Irdeto, to present a multi-vendor automotive cybersecurity solution for OEMs and fleet managers. At the IAA New Mobility World in Frankfurt on September 12-17, the companies will demonstrate how HARMAN’s SHIELD Platform can be integrated with the IBM QRadar Security Intelligence Platform, Irdeto’s ECU protection solution - Cloakware™ Secure Environment and G+D’s Automotive Security Management Framework to cyber-shield connected cars. As part of HARMAN’s ongoing commitment to keep connected cars protected against cyber-attacks, such as ransomware attempts, the integrated system brings together four industry-leading solutions to deliver complete visibility, detection and mitigation capabilities. HARMAN and IBM recently combined their respective SHIELD and QRadar platforms in June 2017 to deliver an end-to-end solution that offers on-board detection and mitigation of a range of security threats, along with a robust backend analysis and forensic system. G+D’s Automotive Security Management Framework (ASMF) is a dedicated security management platform to provide security provisioning services for various connected car and mobility use cases. By adding Secure Environment, a component of Cloakware™ for Automotive by Irdeto along with G+D’s Automotive Security Management Framework (ASMF), these capabilities will be expanded and enhanced, allowing OEMs and fleet managers to establish a strong security foundation in their vehicles from the start. During the event the partners will highlight how the integration between their respective offerings can help address a ransomware attack taking place in real-time, by demonstrating the seamless integration between the four companies. With over 200 speakers and 950,000 attendees, IAA New Mobility World is the leading cross-industry B2B event focused on the future of mobility. The joint automotive cybersecurity solution will be demonstrated at the IBM booth (C30) at Hall 3.1 New Mobility World, between Sept 12-17. “The automotive cybersecurity space has been transitioning in the last year into its maturity phase, and it’s becoming evident that security is a collaborative effort,” says Asaf Atzmon, HARMAN’s director for business development & marketing, automotive cybersecurity. “As the market leader in this space, HARMAN is pleased to extend our partnership ecosystem, working with three security leaders to deliver an industry-first cybersecurity system that will allow OEMs and fleet managers to benefit from a truly defense-in-depth security solution.” About IBM Security IBM Security offers one of the most advanced and integrated portfolios of enterprise security products and services. The portfolio, supported by world-renowned IBM X-Force® research, enables organizations to effectively manage risk and defend against emerging threats. IBM operates one of the world’s broadest security research, development and delivery organizations, monitors 35 billion security events per day in more than 130 countries, and holds more than 3,000 security patents. For more information, please visit www.ibm.com/security, follow @IBMSecurity on Twitter or visit the IBM Security Intelligence blog. About G+D Mobile Security G+D Mobile Security is a global mobile security technology company headquartered in Munich, Germany. The company is part of the Giesecke+Devrient group. G+D Mobile Security manages and secures billions of digital identities throughout their entire life cycle. Our products and solutions are used by commercial banks, mobile network operators, car and mobile device manufacturers, business enterprises, transit authorities and health insurances and their customers every day to secure payment, communication and device-to-device interaction. For more information, please visit: https://www.gi-de.com/mobile-security/. About Irdeto Irdeto is the world leader in digital platform security, protecting platforms and applications for media & entertainment, automotive and IoT connected industries. Our solutions and services enable customers to protect their revenue, create new offerings and fight cybercrime. Irdeto’s software security technology and cyber services protect over 5 billion devices and applications for some of the world’s best-known brands. Our unique heritage as a subsidiary of multinational media group Naspers (JSE: NPN) means that we are a well-established and reliable partner to help build a more secure future. Please visit Irdeto at www.irdeto.com. ABOUT HARMAN HARMAN (harman.com) designs and engineers connected products and solutions for automakers, consumers, and enterprises worldwide, including connected car systems, audio and visual products, enterprise automation solutions; and services supporting the Internet of Things. With leading brands including AKG®, Harman Kardon®, Infinity®, JBL®, Lexicon®, Mark Levinson® and Revel®, HARMAN is admired by audiophiles, musicians and the entertainment venues where they perform around the world. More than 50 million automobiles on the road today are equipped with HARMAN audio and connected car systems. Our software services power billions of mobile devices and systems that are connected, integrated and secure across all platforms, from work and home to car and mobile. HARMAN has a workforce of approximately 30,000 people across the Americas, Europe, and Asia. In March 2017, HARMAN became a wholly-owned subsidiary of Samsung Electronics Co., Ltd. For more information, visit harman.com, follow @HARMAN @HARMANSecurity and @HARMANServices on Twitter. © 2017 HARMAN International Industries, Incorporated. All rights reserved. Harman Kardon, Infinity, JBL, Lexicon and Mark Levinson are trademarks of HARMAN International Industries, Incorporated, registered in the United States and/or other countries. AKG is a trademark of AKG Acoustics GmbH, registered in the United States and/or other countries. Features, specifications and appearance are subject to change without notice.


Grant
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: ICT-16-2015 | Award Amount: 4.60M | Year: 2016

Cloud-LSVA will create Big Data Technologies to address the open problem of a lack of software tools, and hardware platforms, to annotate petabyte scale video datasets. The problem is of particular importance to the automotive industry. CMOS Image Sensors for Vehicles are the primary area of innovation for camera manufactures at present. They are the sensor that offers the most functionality for the price in a cost sensitive industry. By 2020 the typical mid-range car will have 10 cameras, be connected, and generate 10TB per day, without considering other sensors. Customer demand is for Advanced Driver Assistance Systems (ADAS) which are a step on the path to Autonomous Vehicles. The European automotive industry is the world leader and dominant in the market for ADAS. The technologies depend upon the analysis of video and other vehicle sensor data. Annotations of road traffic objects, events and scenes are critical for training and testing computer vision techniques that are the heart of modern ADAS and Navigation systems. Thus, building ADAS algorithms using machine learning techniques require annotated data sets. Human annotation is an expensive and error-prone task that has only been tackled on small scale to date. Currently no commercial tool exists that addresses the need for semi-automated annotation or that leverages the elasticity of Cloud computing in order to reduce the cost of the task. Providing this capability will establish a sustainable basis to drive forward automotive Big Data Technologies. Furthermore, the computer is set to become the central hub of a connected car and this provides the opportunity to investigate how these Big Data Technologies can be scaled to perform lightweight analysis on board, with results sent back to a Cloud Crowdsourcing platform, further reducing the complexity of the challenge faced by the Industry. Car manufacturers can then in turn cyclically update the ADAS and Mapping software on the vehicle benefiting the consumer.


Patent
Ibm | Date: 2012-03-14

In V2V or other networks in which multiple video cameras can share video data, a network participant ordinarily has the option of selecting a particular video data stream (either generated by local cameras or received from other network participants. To facilitate the process of selecting a video data stream for presentation, the users vehicle (in a V2V network) receives video data streams generated by other network participants along with identifiers indicating the video data stream actually being presented to the sender. The receiving system identifies the received video data stream by the greatest number of network participants and displays the identified video data stream on the users in-vehicle video display.

Claims which contain your search:

1. A method for determining which video data stream is to be presented on a video display used by a first participant in a network in which multiple participants produce video data streams that may be shared among other participants, said method comprising: receiving video data streams currently being presented on video displays used by a plurality of other participants; identifying which of said received video data streams is currently being presented on displays used by the greatest number of participants; presenting the identified video data stream on the video display used by said first participant.

2. A method according to claim 1 further comprising: determining whether the video data stream currently being presented on the video display used by said first participant is different than said identified video data stream; providing an indicator to the first participant that the video data stream currently being presented is not said identified video data stream; in response to an input from said first participant, switching the video data stream being presented to said identified video data stream.

3. A method according to claim 1 further comprising automatically switching the video data stream currently being presented on the video display used by said first participant when the video data stream currently being presented on displays used by the greatest number of participants changes from the video data stream currently being presented.

4. A method according to claim 1 wherein said method is performed as the first participant joins the network.

5. A method according to claim 1 wherein said network is a vehicle-to-vehicle network and at least some of said video data streams are produced by vehicle-mounted video cameras.

6. A method according to claim 5 further comprising: receiving an override video data stream produced by another network participant; and replacing the video data stream currently being presented with the received override video data stream.

7. A method according to claim 6 wherein said override video data stream is received from a source outside the vehicle-to-vehicle network.


Patent
Ibm | Date: 2016-06-01

An arrangement to reduce refreezing of meltable snow includes a wiper arm configured to wipe the meltable snow from an ambient-facing surface of a window positioned on a vehicle. The arrangement also includes a sensor coupled to the wiper arm that is configured to gather and transmit a parameter on the vehicle and environment. The arrangement also includes a processor that is configured to receiving the parameter, determining refreezability of the meltable snow based on the parameter, and defining an action to be performed by the wiper arm.

Claims which contain your search:

1. An arrangement to prevent melting of refreezable-meltable snow deposited on an ambient-facing surface of a window positioned on a vehicle, the arrangement comprising: at least one sensor configured togathering at least one parameter;predicting the melting of the refreezable-meltable snow; andactuating a blower to sufficiently cool the window, the sufficiently cooling preventing the melting of the refreezable-meltable snow.

2. The arrangement according to claim 1, wherein the at least one parameter is selected from the group comprising actual window temperature, predicted window temperature, recent driving history, climate control settings of the vehicle, window moisture sensor feedback, vehicle external temperature, vehicle internal temperature, vehicle type, vehicle internal color, vehicle external color, solar radiation intensity at the vehicle, vehicle orientation, snow precipitation type, snow precipitation intensity, wind speed, ambient temperature and parking location history.

3. The arrangement according to claim 1, wherein the at least one parameter is gathered based on an input from a camera installed on the vehicle.

4. The arrangement according to claim 1, wherein the predicting is based on an input from a camera installed on the vehicle.

5. The arrangement according to claim 1 further comprising an actuator actuable by a user of the vehicle to request termination of the blower.

6. The arrangement according to claim 5 further comprising a timer coupled to the actuator, wherein the user configures the timer for the termination of the blower.


News Article | December 15, 2016
Site: www.techrepublic.com

On Thursday, IBM announced that German automaker BMW would be partnering with them at their Internet of Things (IoT) HQ collaboratory in Munich, Germany. The pair will focus on the natural language capabilities of Watson, hoping to use cognitive computing to "personalise the driving experience" and improve driver support, according to a blog post. The partnership is part of a greater $100 million investment in IBM's Watson IoT HQ in Munich, which isn't too far from BMW's Bavarian HQ, the post said. Engineers from both camps will work together on new, Watson-powered tools and services for connected vehicles. Four BMW i8 hybrid sports cars will take up residence at the IBM IoT HQ as part of the deal. IBM's BlueMix cloud platform will act as the foundation for Watson's machine learning and IoT for Automotive to connect to the cars themselves. Watson learns the driver's behaviors, preferences, and habits, and can the modify or customize the driving experience as needed, the post said. "Got a question about how your i8 is performing? Ask your i8 just as you would talk to a friend, and your i8 will reply. Watson will learn the i8 owner's manual and with the natural language capabilities to be able to understand the driver's questions, he'll understand what you're asking and provide answers in a conversational style," the post said. The move is an interesting approach to automotive tech, as IBM still seems to be skirting around the autonomous driving advances that are winning headlines. Instead, it seems that IBM is taking a complementary approach to making the car experience more contextual for the driver and riders. BMW, on the other hand, is planning to test autonomous vehicles in Munich in 2017. "Our insight shows that while the car will remain a fixture in personal transportation, the driving experience will change more over the next decade than at any other time of the automobile's existence," Harriet Green, global head of IBM's Watson IoT business, said in a press release. Basically, the two companies are working on "conversational interfaces between cars and drivers," according to the release. At the risk of making an overly-obvious pop culture connection, think about it like KITT from Knight Rider. The goal is to also add Weather Company data for even more context, the release said. This isn't the first time IBM has integrated Watson into a vehicle. In June, IBM integrated Watson in a similar way into Olli, a self-driving transport bus in Maryland. IBM Watson has also made its way into race cars as a way to help improve performance. And, with the US DOT's recent proposal for V2V communications, connected car technology is likely to become even more important.


News Article | October 27, 2016
Site: co.newswire.com

IQP Corporation, developer of a non-programming Development Environment for cross platform IoT/M2M Apps and Enterprise Applications, will be paired with IBM cloud and server solutions (IBM Bluemix™ / IBM Softlayer™). The IQP platform transforms data from M2M, sensors and devices into IoT-ready apps​ for a wide variety of markets, from connected cars to enterprise apps and smart cities.

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