Savari, Inc.

Harman International Industries, Inc.

T K Holdings, Inc.

Faroog Ibrahim

Kazutoshi Nobukawa

Katta Vidya Sagar

Michael J Higgins-Luthman

Chauhan Tanuj Shashikantbhai

Navin Chandra Rao Katta

Chenikkayala Nagadevendra

Veeranna Ashokappa Halannanavar

Varun Palathour Srirama

This provides methods and systems for the global navigation satellite system (GNSS) combined with the dead-reckoning (DR) technique, which is expected to provide a vehicle positioning solution, but it may contain an unacceptable amount of error due to multiple causes, e.g., atmospheric effects, clock timing, and multipath effect. Particularly, the multipath effect is a major issue in the urban canyons. This invention overcomes these and other issues in the DR solution by a geofencing framework based on road geometry information and multiple supplemental kinematic filters. It guarantees a road-level accuracy and enables certain V2X applications which does not require sub-meter accuracy, e.g., signal phase timing, intersection movement assist, curve speed warning, reduced speed zone warning, and red-light violation warning. Automated vehicle is another use case. This is used for autonomous cars and vehicle safety, shown with various examples/variations.

Positioning system based on geofencing framework

Self-driving and autonomous vehicles are very popular these days for scientific, technological, social, and economical reasons. In one aspect of this technology, path prediction and target classifications are main pillars and enablers for safety and mobility applications. Accurate Estimation of the lane assignment of the remote vehicle with respect to the host vehicle lane results in the accurate threat assessment. V2X and C-V2X are emerging technology for safety applications, and they cover relatively long range. Long range means more room for error in path prediction/target classification. Therefore, here, we offer an efficient path prediction and target classification algorithm to enable these technologies performing safety and mobility applications. The method fuses target vehicles data and host vehicle data in an efficient algorithm to perform this task. Other variations are also presented here.

Vehicle path prediction and target classification for autonomous vehicle operation

This provides methods and systems for V2X applications, such as forward collision warning, electronic emergency brake light, left turn assist, work zone warning, signal phase timing, and others, mainly relying on a GNSS positioning solution transmitted via the Dedicated Short-Range Communications (DSRC) to/from the roadside units and onboard units in other V2X-enabled vehicles. However, the positioning solution from a GNSS may be deteriorated by noise and/or bias due to various error sources, e.g., time delay, atmospheric effect, ephemeris effect, and multipath effect. This offers a novel quality filter that can detect noise and the onset of drift in GNSS signals by evaluating up to four metrics that compare the qualities of kinematic variables, speed, heading angle change, curvature, and lateral displacement, obtained directly or derived from GNSS and onboard vehicle sensors. This is used for autonomous cars and vehicle safety, with various examples/variations.

Positioning quality filter for the V2X technologies

This presentation provides methods to track pedestrians heading angle using smart phone data. Tracking heading angle especially at or from stationary position is key for pedestrian safety, e.g., for smart cross system and pedestrian collision mitigation system. It provides pedestrian-to-vehicle (P2V) platform. It deploys smart phones or mobile devices, equipped with DSRC (Dedicated short range communication) support, to act as beacons for pedestrians: Phone can alert driver to pedestrian presence in path; Pedestrian Basic Safety Message (BSM) can aid awareness for vehicles; It can be used for bicycles, as well. It also provides pedestrian-to-infrastructure (P2I) platform. Smart phone, through DSRC/cellular, transmits pedestrian presence to crosswalks/signals: It enables advanced crosswalk lighting/warning scheme; It enables bundling of pedestrian presence to vehicles. In this presentation, we provide various examples and variations on these.

Methods of tracking pedestrian heading angle using smart phones data for pedestrian safety applications

We introduce a connected vehicles adaptive security signing and verification methodology. We also introduce an adaptive node filtering at receiver, using noise levels and received signal strength. This invention addresses two important pillars in connected vehicle technology and autonomous cars: The first one is related to the security 1609.2 format and its main two functions: signing and verification. The second one explains how the noise level and signal strength can be used to filter undesired connected nodes. In this presentation, we provide various examples and variations on these topics.

Connected vehicles adaptive security signing and verification methodology and node filtering

DSRC (Dedicated short range communication) is expected to play a significant role in Transportation applications for Public Safety and Traffic Management. Some of the key applications especially safety and mobility application requires an accurate representation of the road segments. Accordingly, here, in one example, we describe a method and infrastructure for DSRC V2X (vehicle to infrastructure plus vehicle) system. This presentation, e.g., adds the following improvements on the existing technologies, as some of the examples: (a) Using Speed-Profiles for identifying Intersections/mandatory-stops/Speed-limits, etc. Also, the extension of the map coverage using speed profile data. (b) Vehicular density identification for determining confidence of generated MAP. (c) Mechanisms for identifying Lane Attributes, like Lane-Width, Lane-Connections, Possible Movement states, average travel-time on the lane, etc. In this presentation, we provide various methods and systems to manage and update such maps and its corresponding information, plus many variations on those.

System and method for creating, storing, and updating local dynamic MAP database with safety attribute

Lane Boundary Estimation and Host Vehicle Position and Orientation, within the host lane estimation, using V2V (vehicle to vehicle) system, are discussed here. Lane boundary detection and tracking is essential for many active safety/ADAS application. The lane boundary position enables the tracking of the host vehicle position and orientation inside the lane. It also enables classifying in-lane, adjacent lanes, and other lanes vehicles. These two functionalities (lane boundary estimation and vehicle lane classifications) enable active safety applications (such as LDW, FCW, ACC, or BSD). It also enables the lateral control of the vehicle for lane keeping assist system, or for full lateral control for automated vehicle (automated for one or multiple lane changes).

System and method for lane boundary estimation and host vehicle position and orientation

In one example, we describe a method, system, and infrastructure for dealing with lane-level matching problem, and provide different methods of estimating position corrections, position and map matched confidence, self-correcting map matching (i.e., Map Matching Algorithm (at lane-level)), and turning and lane change events. On the top of the described algorithms, the V2V data, when available, can be used to help the entire discussed algorithm steps. If the relative GPS accuracy between the host vehicle and the remote vehicles in the proximity of the host vehicle is enough to separate vehicle in lane, and there are enough vehicles to exist in all lanes of interest, then it becomes an easier job to determine which lane the host vehicle is in, and this can reflect very positively on all the above algorithm steps.

System and method for map matching

In one example, we describe a method and infrastructure for DSRC V2X (vehicle to infrastructure plus vehicle) system. In one example, some of connected vehicle applications require data from infrastructure road side equipment (RSE). Examples of such applications are road intersection safety application which mostly requires map and traffic signal phase data to perform the appropriate threat assessment. The examples given cover different dimensions of the above issue: (1) It provides methods of RSE of interest selection based solely on the derived relative geometric data between the host vehicle and the RSE's, in addition to some of the host vehicle data, such as heading. (2) It provides methods of RSE of interest selection when detailed map data is communicated or when some generic map data is available. (3) It provides methods of RSE of interest selection when other vehicles data is available. Other variations and cases are also given.

System and method for road side equipment of interest selection for active safety applications

In one example, we describe a method and infrastructure for DSRC V2X (vehicle to infrastructure plus vehicle) system. This can cover a communication circle up to 800 m, and in some cases 1000 m, and as a result, in congested traffic areas, the onboard unit is communicating with high number of units and may end up saturating its processing capability very quickly. In one example, the task is to provide different levels of node filtering algorithms to intelligently select the node data to be processed. This results in optimally using the available processing power by only processing the data of the desired nodes. This method is based on combination of range, velocity, heading, direction, transmitted power, received power threshold, and map database, if available. This also reduces the V2X communication congestion problem resulted in high number of one-to-many nodes communication.

System and method for node adaptive filtering and congestion control for safety and mobility applications toward automated vehicles system

A driver assistance system includes a stereo vision system that includes at least one camera and at least one sensor disposed on or in a vehicle; a roadside marker detection unit configured to receive stereo image data from the stereo vision system, and to detect roadside markers from the stereo image data; and a road path estimation unit configured to estimate a road path in a direction of travel of the vehicle, based on the roadside markers detected by the roadside marker detection unit.

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Dearborn Heights, MI, United States of America

Faroog Ibrahim