Awareness of Road Scene Participants for Autonomous Driving

Abstract

This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomous driving. DATMO provides awareness of road scene participants, which is important in order to make safe driving decisions and abide by the rules of the road. Three main classes of DATMO approaches are identified and discussed. First is the traditional approach, which includes data segmentation, data association, and filtering using primarily Kalman filters. Recent work within this class of approaches has focused on pattern recognition techniques. The second class is the model based approach, which performs inference directly on the sensor data without segmentation and association steps. This approach utilizes geometric object models and relies on non-parametric filters for inference. Finally, the third class is the grid based approach, which starts by constructing a low level grid representation of the dynamic environment. The resulting representation is immediately useful for determining free navigable space within the dynamic environment. Grid construction can be followed by segmentation, association, and filtering steps to provide object level representation of the scene. The chapter introduces main concepts, reviews relevant sensor technologies, and provides extensive references to recent work in the field. The chapter also provides a taxonomy of DATMO applications based on road scene environment and outlines requirements for each application.

Hybrid sampling Bayesian Occupancy Filter

“…Probabilistic methods enable to formally introduce in the models the uncertainties and prior knowledge, and have led to the development of efficient techniques, especially in static environments. When confronted to moving objects, many new issues are raised, the field of dynamic environment modeling still being active [2]. A classic way to address this problem is to adopt an objectbased representation, which leads to multiple target object tracking literature [3], [4].…”

Data fusion of radar and stereo vision for detection and tracking of moving objects

“…Like radar and sonar, lidar measurements are in range-bearing form. Lidars retrieve distance by measuring the time-of-flight of a transmitted light beam [4]. The sensor exhibits excellent angular resolution and accurate range measurements, enabling lidar's use in high performance perception systems.…”

“…Typical properties that describe each cell, such as velocity and/or occupancy, are subsequently estimated from sensor measurements. In the context of advanced driver assistance system (ADAS), a local grid is typically used to represent the area in front of the vehicle [4,43]. The grid then serves the purpose of modelling potential dangers rather than building and maintaining a global map [4].…”

“…Methods that proceed in the described manner are categorised as traditional DATMO [4]. Data segmentation aims at the division of sensor data into meaningful pieces such as points, point clusters or line features.…”

“…Variations to traditional DATMO include model-and grid-based approaches [4]. Modelbased DATMO avoids the difficult problem of data association by incorporating an object model that dictates measurement to target correspondences.…”