Abstract: A method and system for multi-object tracking is set forth. Object data for boundaries of a plurality of objects are received. Poisson multi-Bernoulli mixture filtering is performed on the object data to form a filtered set of object data. Ultimately, the filtered set of object data is used to control the operation of the vehicle. Identifiers and probabilities are associated with the objects to reduce the set of object data.

Abstract: Accurate vehicle localization is arguably the most critical and fundamental task for autonomous vehicle navigation. While dense 3D point-cloud-based maps enable precise localization, they impose significant storage and transmission burdens when used in city-scale environments. A highly compressed representation for LiDAR maps, along with an efficient and robust real-time alignment algorithm for on-vehicle LiDAR scans, is proposed here. The proposed mapping framework, requires less than 0.1% of the storage space of the original 3D point cloud map. In essence, mapping framework emulates an original map through feature likelihood functions. In particular, the mapping framework models planar, pole and curb features. These three feature classes are long-term stable, distinct and common among vehicular roadways. Multiclass feature points are extracted from LiDAR scans through feature detection.

Abstract: A method and system for multi-object tracking is set forth. Object data for boundaries of a plurality of objects are received. Poisson multi-Bernoulli mixture filtering is performed on the object data to form a filtered set of object data. Ultimately, the filtered set of object data is used to control the operation of the vehicle. Identifiers and probabilities are associated with the objects to reduce the set of object data.

Abstract: A method and system of operating a vehicle includes a first sensor generating first sensor data for an object comprising a first bounding box from a first sensor. The first sensor data comprising a first confidence score. A second sensor generates second sensor data for the object comprising a second bounding box from a second sensor different than the second sensor. The second sensor data comprises a second confidence score. A bounding box circuit is programmed to generate a third confidence score for the object based on the first sensor data and the second sensor data and utilize the first sensor data, the second sensor data and the third confidence score to control operation of a vehicle system.

Abstract: Advanced automotive active-safety systems, in general, and autonomous vehicles, in particular, rely heavily on visual data to classify and localize objects, most notably pedestrians and other nearby cars, to assist the corresponding vehicles maneuver safely in their environment. However, the performance of object detection methods is anticipated to degrade under challenging rainy conditions. Nevertheless, and despite major advancements in the development of deraining approaches, the impact of rain on object detection has largely been understudied, especially in the context of autonomous systems. This disclosure analyzes this problem space and presents an improved system for detecting objects under rainy conditions.

Abstract: Accurate vehicle localization is arguably the most critical and fundamental task for autonomous vehicle navigation. While dense 3D point-cloud-based maps enable precise localization, they impose significant storage and transmission burdens when used in city-scale environments. A highly compressed representation for LiDAR maps, along with an efficient and robust real-time alignment algorithm for on-vehicle LiDAR scans, is proposed here. The proposed mapping framework, requires less than 0.1% of the storage space of the original 3D point cloud map. In essence, mapping framework emulates an original map through feature likelihood functions. In particular, the mapping framework models planar, pole and curb features. These three feature classes are long-term stable, distinct and common among vehicular roadways. Multiclass feature points are extracted from LiDAR scans through feature detection.

Abstract: Advanced automotive active-safety systems, in general, and autonomous vehicles, in particular, rely heavily on visual data to classify and localize objects, most notably pedestrians and other nearby cars, to assist the corresponding vehicles maneuver safely in their environment. However, the performance of object detection methods is anticipated to degrade under challenging rainy conditions. Nevertheless, and despite major advancements in the development of deraining approaches, the impact of rain on object detection has largely been understudied, especially in the context of autonomous systems. This disclosure analyzes this problem space and presents an improved system for detecting objects under rainy conditions.

Abstract: By exchanging basic traffic messages among vehicles for safety applications, a significantly higher level of safety can be achieved when vehicles and designated infrastructure-locations share their sensor data. While cameras installed in one vehicle can provide visual information for mitigating many avoidable accidents, a new safety paradigm is envisioned where visual data captured by multiple vehicles are shared and fused for significantly more optimized active safety and driver assistance systems. The sharing of visual data is motivated by the fact that some critical visual views captured by one vehicle or by an infrastructure-location are not visible or captured by other vehicles in the same environment. Sharing such data in real-time provides an invaluable new level of awareness that can significantly enhance a driver-assistance, connected vehicle, and/or autonomous vehicle's safety-system.

Abstract: By exchanging basic traffic messages among vehicles for safety applications, a significantly higher level of safety can be achieved when vehicles and designated infrastructure-locations share their sensor data. While cameras installed in one vehicle can provide visual information for mitigating many avoidable accidents, a new safety paradigm is envisioned where visual data captured by multiple vehicles are shared and fused for significantly more optimized active safety and driver assistance systems. The sharing of visual data is motivated by the fact that some critical visual views captured by one vehicle or by an infrastructure-location are not visible or captured by other vehicles in the same environment. Sharing such data in real-time provides an invaluable new level of awareness that can significantly enhance a driver-assistance, connected vehicle, and/or autonomous vehicle's safety-system.

Abstract: The streaming media encoding and routing system employs an encoder circuit that constructs a streaming media sequence as a plurality of sequential frames, each frame comprising a plurality of segments. The encoder circuit has a processor that is programmed to place media information into the plurality of segments of each frame according to a predefined priority and further programmed to order the sequence of said segments within each frame such that the segments are progressively priority-ranked from high priority to low priority to define an EPIQ-encoded packet. A processor tests an incoming packet to determine whether it is EPIQ-encoded.

Abstract: The streaming media encoding and routing system employs an encoder circuit that constructs a streaming media sequence as a plurality of sequential frames, each frame comprising a plurality of segments. The encoder circuit has a processor that is programmed to place media information into the plurality of segments of each frame according to a predefined priority and further programmed to order the sequence of said segments within each frame such that the segments are progressively priority-ranked from high priority to low priority to define an EPIQ-encoded packet. A processor tests an incoming packet to determine whether it is EPIQ-encoded.

Abstract: A computer-implemented method is provided for compressing a collection of images. The method includes: receiving image data for an image ensemble composed of a plurality of images; dividing the image data into a plurality of data blocks; decomposing each data block from the plurality of data blocks into a set of rank-one tensors by estimating each rank-one tensor individually, where each rank-one tensor is represented by three one-dimensional vectors; arranging the rank-one tensors for each of the plurality of data blocks into a two-dimensional matrix, where the decomposer is implemented by computer processor; compressing the two-dimensional matrix using a compression operation; and storing the compressed two-dimensional matrix in a non-transitory data store.

Abstract: A computer-implemented method is provided for compressing a collection of images. The method includes: receiving image data for an image ensemble composed of a plurality of images; dividing the image data into a plurality of data blocks; decomposing each data block from the plurality of data blocks into a set of rank-one tensors by estimating each rank-one tensor individually, where each rank-one tensor is represented by three one-dimensional vectors; arranging the rank-one tensors for each of the plurality of data blocks into a two-dimensional matrix, where the decomposer is implemented by computer processor; compressing the two-dimensional matrix using a compression operation; and storing the compressed two-dimensional matrix in a non-transitory data store.

Abstract: A computer-implemented method for resizing an image using a seam carving algorithm. The method may include measuring energy levels of pixels in an original image to derive an original energy map; applying a filter to an original energy map to derive a first energy map having a scale less than the original energy map; iteratively applying the filter N times, starting with the first energy map, to an energy map from an immediately preceding iteration; upsampling each of the energy maps to a resolution that matches the original energy map; combining the upsampled energy maps with the original energy map to form a composite image; identifying a seam by finding a path in the composite image having lowest energy quantities from one end of the composite image to an opposing end of the composite image; and selectively deleting the identified seam from the original image, thereby yielding a resized image.

Abstract: There is provided a channel capacity estimation method for adaptive video transmission, the method including: estimating a Bit Error Rate (BER) for a received video packet; and estimating channel capacity by using the estimated BER.

Abstract: There is provided a channel capacity estimation method for adaptive video transmission, the method including: estimating a Bit Error Rate (BER) for a received video packet; and estimating channel capacity by using the estimated BER.

Abstract: A corrupted packet toleration and correction system includes a receiver adapted to employ a cross layer protocol that distinguishes between corrupted packets and error-free packets, and tolerates corrupted packets by making side information about corrupted packets available to an application layer. A decoder of the application layer provides hybrid decoding that simultaneously handles errors and erasures and takes advantage of the side information, including employing LDPC (HEEL) based codes over short packet blocks in the cross layer protocol.

Abstract: A method is provided for estimating the header of a data packet in a wireless communication system. The method includes: maintaining a list of data packets received without an error at a receiver; receiving at the receiver a corrupt data packet having errors in its header; computing a likelihood score for the header of the corrupt data packet in relation to each entry in the list of data packets; and selecting an entry having the highest likelihood score as an estimated header for the corrupt data packet.

Abstract: A worm propagation modeling system for use with a mobile ad-hoc network (MANET) includes an infection detection module receiving temporal dynamics information relating to temporal dynamics of worm spread in the MANET and spatial dynamics information relating to spatiality of nodes in the MANET. The infection detection module detects infection in a network segment of the MANET based on the temporal dynamics information and the spatial dynamics information.

Abstract: An improved routing scheme is provided for data packets in a network. Upon receiving incoming data packets at an intermediate routing node of the network, the incoming data packets are processed in accordance with a cross-layer protocol. In addition, network coding is applied to the incoming data packets to form a combined data packet which is in turn broadcast over the network.