Abstract: An imaging system is described for generating an estimate for a virtual horizon for a moving vehicle. The estimate is based on a lidar point cloud and on pitch rate data from a gyroscope. The lidar point cloud data estimates the horizon based on lidar scans that are updated at a first rate. The gyroscope data is updated at a second rate that is faster than the first rate and therefore can be used to augment the point cloud based horizon estimation to produce a more accurate horizon estimation when the vehicle is pitching at a high rate. The gyroscope data can also be used to correct individual point clouds, which may be distorted if the vehicle is pitching or rolling during an individual scan.

Abstract: An imaging system is described for generating an estimate for the virtual horizon for a moving vehicle. The estimate of the virtual horizon can correspond to lower and higher boundaries of a region within the field of regard, such that the virtual horizon is between the lower and the higher boundaries, in cases where determination of the virtual horizon may be unreliable due to traffic, weather or other road conditions that obscure the visibility in front of the vehicle the imaging system may switch to a static vertical scan density pattern having a broad central focus, which can mitigate the possibility that the system focuses on an incorrect virtual horizon and fails to capture significant objects or conditions in the roadway.

Abstract: A method for managing tracklets in a particle filter estimation framework includes executing a tracklet prediction dependent on a list of previous tracklets, thereby determining persistent tracklets and new tracklets; sampling new measurements for initializing the new tracklets, thereby determining an amount of estimated new tracklets; and determining an amount of the persistent tracklets dependent on the list of previous tracklets. The method further includes determining an amount of the new tracklets and an amount of updated persistent tracklets to be sampled dependent on the amount of estimated new tracklets, the amount of the persistent tracklets, and a memory bound; sampling the updated persistent tracklets from a list of the persistent tracklets dependent on the determined amount of the updated persistent tracklets; and sampling the new tracklets from unassociated measurements dependent on the determined amount of the new tracklets.

Abstract: A method for managing tracklets in a particle filter estimation framework includes executing a tracklet prediction dependent on a list of previous tracklets, thereby determining persistent tracklets and new tracklets; sampling new measurements for initializing the new tracklets, thereby determining an amount of estimated new tracklets; and determining an amount of the persistent tracklets dependent on the list of previous tracklets. The method further includes determining an amount of the new tracklets and an amount of updated persistent tracklets to be sampled dependent on the amount of estimated new tracklets, the amount of the persistent tracklets, and a memory bound; sampling the updated persistent tracklets from a list of the persistent tracklets dependent on the determined amount of the updated persistent tracklets; and sampling the new tracklets from unassociated measurements dependent on the determined amount of the new tracklets.