describe the road network. e method estimates the road course and road width by matching the digital road map and a sensor-based grid map. One of the most important step of the algorithm is the detection of road borders. is step is however largely sensor-dependent and is of less interest in the discussion here. In the next step, the road border hypotheses are compared with the digital map features. ese features are estimated from two elements of the map: road width and road centre line.
e matching is then approached as an optimisation problem trying to minimise given error criterion.
is measure takes into account the D position of the vehicle, its heading and the road width.
. Automatic perception enhanced by map data
(a) Map with robot position. (b) Map projected on image plane. (c) Actual camera capture.
Figure . – Camera-based approach enhanced with map data. Source: Irie and Tomono ().
.. Vision-based perception enhanced with maps
e majority of research in automatic perception is based on mimicry of human drivers. While driv- ing, our main source of information are our eyes, so vision-based algorithms are methods that come naturally when devising an autonomous car. Unfortunately, the imitation of driver’s behaviour ends there, on using cameras. Several authors have gone a step further however and tried to use map data as we, humans, use our memory.
An interesting approach for improving the performance of camera-based algorithms concerns the visibility of interest points. Alcantarilla et al. () presented a machine learning method to predict the visibility of known D points with respect to a query camera. e approach has been applied to large- scale urban environments and, at boom, it goes back to exploiting geometric relationships between the
D map and camera poses. Additionally, the algorithm takes advantage of appearance information from multiple neighbouring cameras. Predicting visible points shows two at least two immediate benefits.
Firstly, knowing the visible zones permits to focus on them and limit necessary computation, which in turn speeds up the whole process. Secondly, limiting the amount of processed data proves beneficial, both in terms of robustness and accuracy, for the data association between known points and features detected by the camera.
Another novel approach for traffic perception limits itself to use a single-camera system. e method proposed by Irie and Tomono () is intended for mobile navigation of outdoor robots. e approach exploits digital street maps along with the robot position and prior knowledge of the environment, as illustrated by Figure.. e image processing part uses the technique ofsuperpixels, i.e. an input image is over-segmented and then these superpixels are grouped into various semantic classes, e.g.
carriageway, pavement, wall etc. e algorithm is divided in two complementary parts: classification and localisation. e first part is formulated as an energy minimisation problem in which the authors have employed graph cuts to estimate the optimal class for each superpixel of the image. e obser- vation are combined with the prior information coming from the map using the maximum a posterior (MAP) estimation. Due to the fact that erroneous information from map can lead to false recognition, the localisation information is incorporated into the classification result. e authors have used the map from OpenStreetMap (OSM) project. For the needs of their method, they extracted information about roadway surface, buildings and sidewalks.
Cappelle et al. () use a DGISdatabase with geo-localised images for both localisation and per- ception. In order to perceive dynamic obstacles in the vehicle environment, the approach exploits the differences between real (acquired) image and virtual (from database) ones. Images acquired by the on-board camera may contain obstacles which are absent in the D model; when the inverse situation happens, the map is probably faulty.
.. Perception using laser-range sensors
An important application in road perception is the detection of road lanes. First works in the automotive domain used D lidar data. By exploiting provided sensor information, Ogawa and Takagi () proposed a method detecting lane marks and other objects. e novelty in their approach was to use both range and reflectivity data. e method applies an Extended Kalman Filter (EKF) based on the movement of the vehicle and the detected lane position. e problem of detecting pedestrians which are much less predictable in their movement has been the subject of many works. Among others, the same authors, Ogawa, Sakai, et al. (), presented an approach for pedestrian recognition only using an on-vehicle lidar.
A complementary method for vehicle position detection was described in (Takagi et al.). A D lidar is used as a forward object detection sensor. In addition, the authors describe a method of coordinating lidar-based detections with a map in order to create a highly accurate navigation system.
e first research work that used OpenStreetMap (OSM) data for all parts of an autonomous vehicle has been done by Hentschel and Wagner (). e map knowledge was integrated into robotic tasks, ranging from localisation and trajectory planning to autonomous vehicle control. e authors went even further by proposing to apply standardised geodata from theOSMproject as the environmental representation for intelligent vehicles. e idea of the approach is based on detecting surrounding buildings with a lidar sensor. en, this information is combined with the extracted map data in order to obtain fine-grained localisation estimate. e authors opted for a solution using aGPSposition fix filtered using Kalman filtering together with wheel odometry andIMUdata. e pose obtained in such a manner is then integrated into a particle filter. In order to combine the data from a lidar (in form of
D point clouds) with a map, the method of virtual scans has been employed. is approach permied the authors to extract two-dimensional landmark information about, e.g. vertical planes, from a D scan.
Velodyne lidar is a powerful sensor capable of providing over million cloud points per second. Many perception algorithms take advantage of the high level of detail, long range and high precision of clouds obtained by this device. e team working on MuCAR- autonomous ground vehicle presented an efficient lidar-based D object perception method (Himmelsbach, Müller, et al.). In further work (Himmelsbach, Lueel, et al.), this approach has been developed to enable the automotive system to navigate autonomously.
Providing an exact description of the environment and understanding the scene in which the vehicle evolves has been the subject of (Stiller and Ziegler ). Situation recognition algorithm is based on Markov logic networks and employs as well topological and geometrical reasoning. e choice of trajectory is done based on a quality measure. is measure takes into consideration factors like driver safety, passenger comfort and, obviously, the efficiency of following the reference path. is approach was designed for a priori unknown environments and implemented on the AnnieWAY vehicle that won the Grand Cooperative Driving Challenge. Given map priors, the authors proposed another algorithm that considers map knowledge in order to improve the driving performance (Ziegler et al.
). e map is highly usage-specific and contains hints about the road priority or speed limitations.
e main geographical knowledge is a database of geo-referenced visual landmarks, such as road signs and markings.
On the other hand, one can mention some works that state explicitly that the use map data, lidars or GNSSsensors is not the best way to move on. Geiger, Lauer, Wojek, et al. () suggests as a solution
. Automatic perception enhanced by map data a bio-mimetic approach, based solely on visual clues.