Abstract-Evidential grids have recently been shown to have interesting properties for mobile object perception. Possessing only partial information is a frequent situation when driving in complex urban areas, and by making use of the Dempster-Shafer framework, evidential grids are able to handle partial information efficiently. This article deals with a lidar perception scheme that is enhanced by geo-referenced maps used as an additional source of information in a multigrid fusion framework. The paper looks at the key stages of such a data fusion process and presents an adaptation of the conjunctive combination rule for refining the analysis of conflicting information. This method relies on temporal accumulation to distinguish between stationary and moving objects, and applies contextual discounting for modeling information obsolescence. As a result, the method is able to better characterize the state of the occupied cells by differentiating moving objects, parked cars, urban infrastructure and buildings. Another advantage of this approach is its ability to separate the drivable from the non-drivable free space. Experiments carried out in real traffic conditions with a specially equipped car illustrate the performance of this approach. utonomous driving is still a challenge, despite significant progress that has been made recently [1] . Perception and understanding of the dynamic scene around the vehicle is a key issue for autonomous navigation [2] . The problem of modeling a complex, dynamic environment and capturing in real time the information needed for safe driving is still an open issue, in particular when the sensors are deeply integrated in the vehicle. The final decision stage is also of importance, especially when dealing with uncertain or inaccurate data. Urban environments, where good scene understanding is crucial, present some of the biggest challenges. Trajectories of mobile objects are hard to predict because of the high variability of their speeds and directions, and moving traffic gives rise to frequent occlusions in the field of view of the sensors. The sheer number of mobile objects also poses computational problems, e.g. for tracking algorithms. At the same time, more and more detailed and precise geographic databases are becoming available. A substantial amount of research has focused on the mapping problem solved, for instance, simultaneously with localization. This article presents a perception method that uses the meta-knowledge from a digital map to enhance the scene understanding capability of an autonomous vehicle in terms of drivable space characterization and mobile object detection. It is important to identify free space for path planning, but it is also important to distinguish static objects (e.g. road signs) from mobile road users (cars, cyclists, pedestrians) who are often vulnerable. If a mobile object is detected at the roadside, the vehicle can reduce its speed or modify its trajectory to avoid a possible collision. To model the vehicle environment, the approach presented here uses multiple 2D occupancy grids [3] . The information processing is based on the Dempster-Shafer theory of evidence and pignistic decisions. The map is treated as an additional source of information, on a par with the other sensors. We propose a method for incorporating this prior knowledge in an embedded perception system intended for autonomous navigation. Digital maps have been used as prior knowledge for several years already [4] . The use of drivable road maps for localization [5], [6], perception [7] , navigation and driving [8] has proved to be of high added value. Geodata was also successfully used for mobile navigation in [9] . Some research works have also contributed to the map updating problem [10] . In a previous paper, we have focused on the use of highly accurate and precise special-purpose maps [11] This paper describes a unified approach applicable to a variety of problems based on spatial representation of the environment using Dempster-Shafer theory. This theory was originally applied to occupancy grids [14] , and it is only recently that researchers have made use of it in the context of mobile perception in dynamic environments [15] . The underlying idea of the evidential framework is that information is processed via the merging of different map grids with no conditioning and no intermediate decision stages: the decision making is only applied at the very last stage. In comparison to previous research, this method enables us to distinguish between drivable and non-drivable free space. This article is organized as follows. Section II provides necessary theoretical background regarding the DempsterShafer theory of evidence. In Section III we describe the proposed method in detail, starting with the description of the required data and the purpose of each grid. We also give details regarding information fusion. Data-dependent computations which are not central to the method are described in Section IV-A. Sections IV-B and IV-C present results obtained with real-world data. Finally, Section V concludes the paper and presents ideas for future work.
In REF the grid map created by a laser scanner is enhanced by the fusion with a geo-referenced map.
Map-Aided Evidential Grids for Driving Scene Understanding
IEEE Intelligent Transportation Systems Magazine
IEEE Intelligent Transportation Systems Magazine
Engineering Computer Science