Abstract. Accurate and continuous vehicle localization in urban envi-ronments has been an important research problem in recent years. In this paper, we propose a landmark based localization method using road signs and road markings. The principle is to associate the online detec-tions from onboard cameras with the landmarks in a pre-generated road infrastructure database, then to adjust the raw vehicle pose predicted by the inertial sensors. This method was evaluated with data sequences ac-quired in urban streets. The results prove the contribution of road signs and road markings for reducing the trajectory drift as absolute control points.

rithm for an autonomous car based on an integration of multiple sensors data. The sensor system is composed of onboard motion sensors, a low-cost GPS receiver, a precise digital map, and multi-ple cameras. Data from the onboard motion sensors, such as yaw rate and wheel speeds, are used to predict the vehicle motion, and the GPS receiver is applied to establish the validation boundary of the ego–vehicle position. The digital map contains location information at the centimeter level about road surface markers (RSMs), such as lane markers, stop lines, and traffic sign markers. The multiple images from the front and rear mono-cameras and the around-view monitoring system are used to detect the RSM features. The localization algorithm updates the measurements by matching the RSM features from the cameras to the digital map based on a particle filter. Because the particle filter updates the measurements based on a probabilistic sensor model, the exact probabilistic modeling of sensor noise is a key factor to enhance the localization performance. To design the probabilistic noise model of the RSM features more explicitly, we analyze the results of the RSM feature detection for various real driving conditions. The proposed localization algorithm is verified and evaluated through experiments under various test scenarios and configurations. From the experimental results, we conclude that the presented localization algorithm based on the probabilistic noise model of RSM features provides sufficient accuracy and reliability for autonomous driving system applications. Index Terms—Precise localization, multiple cameras, road sur-face marker, probabilistic noise modeling, probabilistic noise model of road surface marker (RSM) features, particle filtering, autonomous car, autonomous driving. I.

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