Abstract: 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.

Abstract: Self-driving and autonomous vehicles are very popular these days for scientific, technological, social, and economical reasons. In one aspect of this technology, one of the main concerns for an implementation of any V2X technology on a large scale is the issue of congestion control. In large cities and crowded highways during rush hours, each host vehicle can get messages from over 200 other vehicles and several road side units, all working on the same channel and trying to send and receive messages at the same time. With respect to the weather effect on signal, the signal path loss occurs whenever there is moderate (or moderate plus) rain, and because of that, the OBU communications packets are prone to get lost, or communication coverage region gets diminished, depending upon the intensity, speed, angle and temperature of the rainfall/snowfall droplets. We have provided the solutions for these 2 problems, with variations.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.