Map-Aided Evidential Grids for Driving Scene Understanding

International audienceEvidential grids have recently shown interesting properties for mobile object perception since the Dempster– Shafer framework allow them to handle efficiently partial information which is a frequent situation when driving in complex urban areas. This article deals with a lidar perception scheme that is enhanced by geo-referenced maps used as an additional source of information in a multi-grid fusion frame-work. The paper presents the key stages of such a data fusion process. An adaptation of the conjunctive combination rule is presented to refine the analysis of the conflicting information. The method relies on temporal accumulation to make the distinction between stationary and moving objects, and applies contextual discounting for modelling information obsolescence. As a result, the method is able to better characterise the state of the occupied cells by differentiating moving objects, parked cars, urban infrastructure and buildings. Another output of this approach is the capability to separate the navigable space from the non-navigable one. Experiments carried out on real traffic conditions with an equipped car illustrate the performance of such an approach

Object-level fusion for surround environment perception in automated driving applications

Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. The detection of dynamic objects is one of the most important aspects required by advanced driver assistance systems and automated driving. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system, where processing occurs in three levels: sensor, fusion and application. A complete and consistent object model which includes the object’s dynamic state, existence probability and classification is defined as a sensor-independent and generic interface for sensor data fusion across all three processing levels. Novel algorithms are developed for object data association and fusion at the fusion-level of the architecture. An asynchronous sensor-to-global fusion strategy is applied in order to process sensor data immediately within the high-level fusion architecture, giving driver assistance systems the most up-to-date information about the vehicle’s environment. Track-to-track fusion algorithms are uniquely applied for dynamic state fusion, where the information matrix fusion algorithm produces results comparable to a low-level central Kalman filter approach. The existence probability of an object is fused using a novel approach based on the Dempster-Shafer evidence theory, where the individual sensor’s existence estimation performance is considered during the fusion process. A similar novel approach with the Dempster-Shafer evidence theory is also applied to the fusion of an object’s classification. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. A thorough evaluation of the complete object model is performed on a closed test track using vehicles equipped with hardware for generating an accurate ground truth. Existence and classification performance is evaluated using labeled data sets from real traffic scenarios. The evaluation demonstrates the accuracy and effectiveness of the proposed sensor data fusion approach. The work presented in this thesis has additionally been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic