Abstract: A vehicle system of a vehicle may include a controller programmed to receive sensor data from one or more vehicle sensors as the vehicle drives along a road, determine whether the vehicle is approaching a road anomaly based on the sensor data, and upon determination that the vehicle is approaching the road anomaly, begin recording audio and continue recording audio until after the vehicle interacts with the road anomaly.

Abstract: A server for communication-efficient model aggregation in federated networks for connected vehicle applications is provided. The server includes a controller programmed to: obtain contributions of a plurality of vehicles in a federated learning framework; determine weights for local gradients received from the plurality of vehicles based on the contributions; adjust the weights based on a comparison of potential functions for the plurality of vehicles; and aggregate the local gradients based on the adjusted weights to obtain a global model.

Abstract: The disclosure generally relates to a system comprising a memory and a processor configured to access the memory and execute the machine-executable instructions stored on the memory to determine that a target vehicle is engaging in an abnormal driving, determine that the abnormal driving exceeds a notification threshold parameter of an abnormal driving notification system, generate a notification, and monitor the ego vehicle and/or driver to alter notification threshold values based on the reactions of the ego vehicle and/or driver inputs.

Abstract: Systems and methods are provided for forming remote vehicular micro clouds at one or more remote locations. According to some embodiments, the methods and systems comprise responsive to receiving a request to form a vehicular micro cloud from a client device, communicating with a plurality of vehicles within an area of a geographic location to collectively form a vehicular micro cloud at the geographic location, where client device is remote from the area of the geographic location. The methods and systems further include receiving resource data from the plurality of vehicles, the resource data comprising detection results of an environment surrounding the geographic location based on sensor sets of the plurality of vehicles, and transmitting the resource data to the client device.

Abstract: An object detection system for a vehicle includes a processor and a memory communicably coupled to the processor. The memory stores instructions that when executed by the processor cause the processor to receive a priority assignment set by an occupant of a vehicle. The priority assignment corresponds to a customization of at least one warning characteristic. The at least one warning characteristic includes a distance threshold. The instructions also cause the processor to detect an object in an external environment of the vehicle and apply the priority assignment to the object. The instructions further cause the processor to issue a warning according to the at least one warning characteristic. According to the distance threshold, the warning is issued when a distance from the vehicle to the object meets the distance threshold.

Abstract: A trailer monitoring system for a vehicle includes a processor and a memory communicably coupled to the processor. The memory stores instructions that when executed by the processor cause the processor to receive, from at least one monocular camera mounted to a vehicle towing a trailer, at least one monocular camera image of at least a portion of the trailer. The instructions also cause the processor to generate at least one depth map based on the at least one monocular camera image. The instructions further cause the processor to identify a trailer irregularity based on the at least one depth map.

Abstract: Systems and methods are provided for detecting when a vehicle engages in anomalous driving behavior and managing nearby vehicles to mitigate risk to the nearby vehicles due to the anomalous driving behavior. According to some embodiments, the methods and systems comprise receiving an indication that an anomalous driving behavior of a first vehicle is detected, and identifying a plurality of vehicles within a geographic area of the first vehicle based on receiving the indication. The methods and systems further include generating an arrangement for the plurality of vehicles based on the detected anomalous driving behavior, determining a target speed for each of the plurality of vehicles based on the determined arrangement and a current speed of each of the plurality of vehicles, and communicating control messages to each of the plurality of vehicles. The control messages comprising the target speed to distribute the plurality of vehicles into the determined arrangement.

Abstract: Systems and methods are provided for programmatically verifying an origin of abnormal driving. Some examples of abnormal driving may include aggressive driving (e.g., tailgating, cut-in lane, etc.), distracted driving (e.g., swerving, delayed reaction, etc.), and reckless driving (e.g., green light running, lane change without signaling, etc.). For example, the systems and methods may receive an identification of a second vehicle performing abnormal driving from an ego vehicle; initiate a verification process of the identification of the abnormal driving; access driving data associated with an origin of the abnormal driving, wherein the driving data includes the ego vehicle and the second vehicle; using the driving data, determine a confirm or deny decision regarding the identification of the second vehicle from the ego vehicle; and provide the confirm or deny decision.

Abstract: A parking assist system for a vehicle includes a processor and a memory communicably coupled to the processor. The memory stores instructions that when executed by the processor cause the processor to detect a parking space for a vehicle. The instructions further cause the processor to identify one or more parking parameters related to parking the vehicle in the parking space. The parking parameters include a point-of-interest associated with the parking space. The instructions further cause the processor to determine a suitability of the parking space based on the one or more parking parameters. Determining the suitability of the parking space includes predicting a point-of-interest-related action to be performed by an occupant after parking the vehicle in the parking space.

Abstract: System, methods, and other embodiments described herein relate to training a model to stylize low-light images for improved perception. In one embodiment, a method includes encoding, by a style model, an input image to identify first content information. The method also includes decoding, by the style model, the first content information into an albedo component and a shading component. The method also includes generating, by the style model, a synthetic image using the albedo component and the shading component. The method also includes training the style model according to computed losses between the input image and the synthetic image.

Abstract: Systems and methods are provided for forming remote vehicular micro clouds at one or more remote locations. According to some embodiments, the methods and systems comprise responsive to receiving a request to form a vehicular micro cloud from a client device, communicating with a plurality of vehicles within an area of a geographic location to collectively form a vehicular micro cloud at the geographic location, where client device is remote from the area of the geographic location. The methods and systems further include receiving resource data from the plurality of vehicles, the resource data comprising detection results of an environment surrounding the geographic location based on sensor sets of the plurality of vehicles, and transmitting the resource data to the client device.

Abstract: System, methods, and other embodiments described herein relate to improving depth prediction for objects within a low-light image using a style model. In one embodiment, a method includes encoding, by a style model, an input image to identify content information. The method also includes decoding, by the style model, the content information into an albedo component and a shading component. The method also includes generating, by the style model, a synthetic image using the albedo component and the shading component. The method also includes providing the synthetic image to a depth model.

Abstract: A method of operating a traffic management system may comprise identifying a vehicle queue in a first lane of a road based on sensor data from one or more connected vehicles traveling along the road, determining driving instructions for a connected vehicle within a queue management region in a second lane, adjacent to the first lane, to create a gap in front of the connected vehicle for a vehicle in the vehicle queue to change lanes into the gap, and transmitting the driving instructions to the connected vehicle.

Abstract: Systems and methods are provided for activating mitigation strategies through a cloud-based system. Embodiments of the systems and methods disclosed herein can provide mitigation strategies to reduce or eliminate the stop-and-go traffic. A control vehicle can activate a mitigation strategy and operate the vehicle in accordance with the mitigation strategy based on stop-and-go waves. The mitigation strategy may comprise maintaining the vehicle at a reference speed.

Abstract: Systems and methods are provided for activating mitigation strategies through a cloud-based system. Embodiments of the systems and methods disclosed herein can provide mitigation strategies to reduce or eliminate the stop-and-go traffic. A control vehicle can activate a mitigation strategy and operate the vehicle in accordance with the mitigation strategy based on stop-and-go waves. The mitigation strategy may comprise maintaining the vehicle at a reference speed.

Abstract: Systems and methods are provided for activating mitigation strategies to reduce or eliminate the stop-and-go traffic. Mitigation strategies can be applied to reduce stop-and-go traffic or prevent a control vehicle from experiencing stop-and-go traffic. The control vehicle can predict when to apply mitigation strategies based on sensor data that can be applied to generate a trajectory for the control vehicle or a preceding vehicle.

Abstract: A system includes a processor and a memory in communication with the processor. The memory has instructions that, when executed by the processor, cause the processor to count, based on sensor data received from vehicles located in a parking lot, the number of non-parked vehicles located in the parking lot within a time period. The instructions further cause the processor to determine an occupancy status of the parking lot based on the number of non-parked vehicles located in the parking lot.

Abstract: The disclosure includes embodiments for forming an on-demand stationary vehicular micro cloud. In some embodiments, a method for a connected vehicle includes receiving event data that describes a set of events configured for triggering a creation of the on-demand stationary vehicular micro cloud. The method includes receiving sensor data that describes a roadway environment. The method includes detecting, based at least in part on the sensor data, an occurrence of at least one event included in the set of events. The method includes modifying an operation of a communication unit of the connected vehicle to initiate the creation of the on-demand stationary vehicular micro cloud to address the occurrence of the at least one event.

Abstract: A method for deploying of a backup roadside unit (RSU) vehicle to provide on-demand RSU services is described. The method may include determining, by a server, a resource specification to deploy a selected backup RSU vehicle to a requested site. The determining of the resource specification may be performed by the server in response to a scene description received from a requestor. The method also includes sending a dispatch instruction to the selected backup RSU vehicle to deploy the selected backup RSU vehicle to the requested site. In this method, a hardware/software capability of the selected backup RSU vehicle is matched to the resource specification determined by the server.

Abstract: Compensation can be made for mismatch in abnormal driving behavior detection between an ego vehicle and its human driver. Sensor data of a surrounding driving environment can be acquired. The acquired sensor data can be analyzed to detect abnormal driving behavior by a vehicle in the surrounding driving environment. Responsive to detecting abnormal driving behavior by the vehicle in the surrounding driving environment, the abnormal driving behavior can be classified as to whether it is severe. If the abnormal driving behavior is classified as not being severe, an amplified version of the abnormal driving behavior can be caused to be presented to the human driver of the ego vehicle.