Abstract: In some examples, a system may receive, from at least one camera of a vehicle, at least one image including a road. The system may further receive vehicle location information including an indication of a location of the vehicle. In addition, the system may receive at least one of historical information from a historical database, or road anomaly information, where the road anomaly information is determined from at least one of a road anomaly database or real-time road anomaly detection. Based on the at least one image, the indication of the location of the vehicle, and the at least one of the historical information or the road anomaly information, the system may generate at least one of a disparity map or a disparity image.

Abstract: In some examples, one or more processors may receive at least one first visible light image and a first thermal image. Further, the processor(s) may generate, from the at least one first visible light image, an edge image that identifies edge regions in the at least one first visible light image. At least one of a lane marker or road edge region may be determined based at least in part on information from the edge image. In addition, one or more first regions of interest in the first thermal image may be determined based on at least one of the lane marker or the road edge region. Furthermore, a gain of a thermal sensor may be adjusted based on the one or more first regions of interest in the first thermal image.

Abstract: In some examples, a system may determine a plurality of candidate routes between a source location of a vehicle and a destination location for the vehicle. Further, the system may determine one or more respective observation zone volumes for each candidate route of the plurality of candidate routes. The system may determine a field of view (FOV) for one or more vehicle sensors onboard the vehicle. In addition, the system may select a route for the vehicle from the plurality of candidate routes based at least on comparing an overlap of the FOV with the respective observation zone volumes for the candidate routes.

Abstract: A system for dynamic image compression in autonomous vehicles with multiple cameras. The cameras of a vehicle may be prioritized based on various features, and different image compression techniques may be assigned to different cameras in the vehicle, based on the priorities of the cameras, to reduce overall processing load of the vehicle.

Abstract: In some examples, a system may receive, from at least one camera of a vehicle, at least one image including a road. The system may further receive vehicle location information including an indication of a location of the vehicle. In addition, the system may receive at least one of historical information from a historical database, or road anomaly information, where the road anomaly information is determined from at least one of a road anomaly database or real-time road anomaly detection. Based on the at least one image, the indication of the location of the vehicle, and the at least one of the historical information or the road anomaly information, the system may generate at least one of a disparity map or a disparity image.

Abstract: In some examples, one or more processors may receive at least one first visible light image and a first thermal image. Further, the processor(s) may generate, from the at least one first visible light image, an edge image that identifies edge regions in the at least one first visible light image. At least one of a lane marker or road edge region may be determined based at least in part on information from the edge image. In addition, one or more first regions of interest in the first thermal image may be determined based on at least one of the lane marker or the road edge region. Furthermore, a gain of a thermal sensor may be adjusted based on the one or more first regions of interest in the first thermal image.

Abstract: Example implementations described herein are directed to depression detection on roadways (e.g., potholes, horizontal panel lines of a roadway, etc.) through using vision sensor to realize improved safety for advanced driver assistance systems (ADAS) and autonomous driving (AD). Example implementations described herein detect candidate depressions in the roadway in real time and adjust the control of the vehicle system according to the detected depressions.

Abstract: In some examples, one or more processors may receive at least one image of a road, and may determine at least one candidate group of pixels in the image as potentially corresponding to debris on the road. The one or more processors may determine at least two height-based features for the candidate group of pixels. For instance, the at least two height-based features may include a maximum height associated with the candidate group of pixels relative to a surface of the road, and an average height associated the candidate group of pixels relative to the surface of the road. In addition, the one or more processors may determine at least one weighting factor based on comparing the at least two height-based features to respective thresholds, and may determine whether the group of pixels corresponds to debris based at least on the comparing.

Abstract: In some examples, one or more processors may receive at least one image of a road, and may determine at least one candidate group of pixels in the image as potentially corresponding to debris on the road. The one or more processors may determine at least two height-based features for the candidate group of pixels. For instance, the at least two height-based features may include a maximum height associated with the candidate group of pixels relative to a surface of the road, and an average height associated the candidate group of pixels relative to the surface of the road. In addition, the one or more processors may determine at least one weighting factor based on comparing the at least two height-based features to respective thresholds, and may determine whether the group of pixels corresponds to debris based at least on the comparing.

Abstract: Example implementations described herein are directed to depression detection on roadways (e.g., potholes, horizontal panel lines of a roadway, etc.) through using vision sensor to realize improved safety for advanced driver assistance systems (ADAS) and autonomous driving (AD). Example implementations described herein detect candidate depressions in the roadway in real time and adjust the control of the vehicle system according to the detected depressions.