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This paper presents track fusion and behavioral reasoning for moving vehicles in close proximity based on the curvilinear coordinates of roadway geometries. The inferred track and behavior of other vehicles can be used to perform safe actions in intelligent vehicle applications and autonomous driving. Vehicle detections from multiple perception sensors are integrated using track-to-track (T2T) fusion based on a cross-covariance method, and this T2T fusion is performed with curvilinear coordinates created using prebuilt roadway geometry on a digital map. The coordinate conversion to curvilinear space has many benefits for behavioral reasoning and tracking, such as constraining problem spaces and dimensions. A machine learning classifier based on a support vector machine is then applied to deduce the behavior of nearby vehicles. The algorithms presented here for track fusion and behavioral reasoning based on curvilinear coordinates have been verified through experiments in various real traffic scenarios. [ABSTRACT FROM AUTHOR]

Detection and tracking of moving objects is one of the most essential functions of autonomous cars. In order to estimate the dynamic information of a moving object accurately, laser scanners are widely used for their highly accurate distance data. However, these data only represent the surface of an object facing the sensor and changes the appearance of an object over time. This change produces unexpected tracking errors of estimated dynamic states. In this paper, in order to minimize the tracking error caused by appearance changes, a tracking algorithm based on L-shaped model switching is proposed. The suggested algorithm is validated in real traffic experiments where position, velocity, and heading angle error were measured by using precise GPS. The L-shape tracking algorithm successfully mitigated the effect of appearance changes and improved estimation performance. [ABSTRACT FROM PUBLISHER]

Tracking and behavior reasoning of surrounding vehicles on a roadway are keys for the development of automated vehicles and an advanced driver assistance system (ADAS). Based on dynamic information of the surrounding vehicles from the tracking algorithm and driver intentions from the behavior reasoning algorithm, the automated vehicles and ADAS can predict possible collisions and generate safe motion to avoid accidents. This paper presents a unified vehicle tracking and behavior reasoning algorithm that can simultaneously estimate the vehicle dynamic state and driver intentions. The multiple model filter based on various behavior models was used to classify the vehicle behavior and estimate the dynamic state of surrounding vehicles. In addition, roadway geometry constraints were applied to the unified vehicle tracking and behavior reasoning algorithm in order to improve the dynamic state estimation and the behavior classification performance. The curvilinear coordinate system was constructed based on the precise map information in order to apply the roadway geometry to the tracking and behavior reasoning algorithm. The proposed algorithm was verified and evaluated through experiments under various test scenarios. From the experimental results, we concluded that the presented tracking and behavior reasoning algorithm based on the roadway geometry constraints provides sufficient accuracy and reliability for automated vehicles and ADAS applications. [ABSTRACT FROM PUBLISHER]

This paper presents a novel curvilinear-coordinate-based approach to improve object and situation assessment performance for highly automated vehicles under various curved road conditions. The approach integrates object information from radars and lane information from a camera with three steps: track-to-track fusion, curvilinear coordinate conversion, and lane assessment. The track-to-track fusion is achieved through a nearest neighbor filter that updates the target state estimation and covariance with the nearest neighbor measurement, and a cross-covariance method that merges the duplicate tracks using error covariance. In order to determine in which lane the fused tracks are located accurately and reliably, the curvilinear coordinate conversion process is performed. The curvilinear coordinates are generated in the form of a cubic Hermite spline lane model from the lane information of the camera. Based on the converted track information and the lane model in the curvilinear coordinates, the probability distribution of the threat levels in each lane is determined though a probabilistic lane association and threat assessment. The developed algorithm is verified and evaluated through experiments using a real-time embedded system. The results show that the proposed curvilinear-coordinate-based approach provides excellent performance of object and situation assessment, in respect of accuracy and computational efficiency, in real-time operation. [ABSTRACT FROM PUBLISHER]