A navigation filter combines measurements from sensors currently available on vehicles-Global Positioning System (GPS), inertial measurement unit, inertial measurement unit (IMU), camera, and light detection and ranging (lidar)-for achieving lane-level positioning in environments where standalone GPS can suffer or fail. Measurements from the camera and lidar are used in two lane-detection systems, and the calculated lateral distance (to the lane markings) estimates of both lanedetection systems are compared with centimeter-level truth to show decimeter-level accuracy. The navigation filter uses the lateral distance measurements from the lidar-and camera-based systems with a known waypoint-based map to provide global measurements for use in a GPS/Inertial Navigation System (INS) system. Experimental results show that the inclusion of lateral distance measurements and a height constraint from the map creates a fully observable system even with only two satellite observations and, as such, greatly enhances the robustness of the integrated system over GPS/INS alone. Various scenarios are presented, which affect the navigation filter, including satellite geometry, number of satellites, and loss of lateral distance measurements from the camera and lidar systems. . The Federal Highway Administration is funding this project and others across the range of issues that are critical to the transportation industry through the Exploratory Advanced Research (EAR) Program. For more information, see the EAR Web site at http://www. fhwa.dot.gov/advancedresearch/about.cfm#focus.

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This paper presents two techniques for determining vehicle pitch and roll with a 3-D lidar which will first autocalibrate itself to the vehicle's axes. The first method presented is based on Euler angles and the second on Gaussian Processes. A 3-antenna Septentrio GPS receiver is used to asses system performance.

A method of estimating a vehicle's attitude in relation to the road surface using only light detection and ranging (lidar) measurements is presented. Gaussian processes, a machine learning technique, is used to relate the measurements of the road surface to the pitch and roll of the vehicle. Testing was performed under normal driving conditions on a test track as well as under high dynamic maneuvers on a skid-pad to assess performance of the algorithm. On-vehicle results show that the attitude calculations are capable of being implemented in a realtime system and have been compared against a multi-antenna GPS attitude measurement for accuracy.

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