Radu Danescu

3D lane detection system based on stereovision

This work presents a 3D lane detection method based on stereovision. The stereovision algorithm allows the elimination of the common assumptions: flat road, constant pitch angle or absence of roll angle. Moreover, the availability of 3D information allows the separation between the road and the obstacle features. The lane is modeled as a 3D surface, defined by the vertical and horizontal clothoid curves, the lane width and the roll angle. The lane detection is integrated into a tracking process. The current lane parameters are predicted using the past parameters and the vehicle dynamics, and this prediction provides search regions for the current detection. The detection starts with estimation of the vertical profile, using the stereo-provided 3D information, and afterwards the horizontal profile is detected using a model-matching technique in the image space, using the knowledge of the already detected vertical profile. The roll angle is detected last, by estimating the difference of the average heights of the left and right lane borders. The detection results are used to update the lane state through Kalman filtering.

High accuracy stereo vision system for far distance obstacle detection

This paper presents a high accuracy stereo vision system for obstacle detection and vehicle environment perception in a large variety of traffic scenarios, from highway to urban. The system detects obstacles of all types, even at high distance, outputting them as a list of cuboids having a position in 3D coordinates, size and speed.

Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision

Accurate and robust lane results are of great significance in any driving-assistance system. To achieve robustness and accuracy in difficult scenarios, probabilistic estimation techniques are needed to compensate for the errors in the detection of lane-delimiting features. This paper presents a solution for lane estimation in difficult scenarios based on the particle-filtering framework. The solution employs a novel technique for pitch detection based on the fusion of two stereovision-based cues, a novel method for particle measurement and weighing using multiple lane-delimiting cues extracted by grayscale and stereo data processing, and a novel method for deciding upon the validity of the lane-estimation results. Initialization samples are used for uniform handling of the road discontinuities, eliminating the need for explicit track initialization. The resulting solution has proven to be a reliable and fast lane detector for difficult scenarios.

Modeling and Tracking the Driving Environment With a Particle-Based Occupancy Grid

Modeling and tracking the driving environment is a complex problem due to the heterogeneous nature of the real world. In many situations, modeling the obstacles and the driving surfaces can be achieved by the use of geometrical objects, and tracking becomes the problem of estimating the parameters of these objects. In the more complex cases, the scene can be modeled and tracked as an occupancy grid. This paper presents a novel occupancy grid tracking solution based on particles for tracking the dynamic driving environment. The particles will have a dual nature-they will denote hypotheses, as in the particle filtering algorithms, but they will also be the building blocks of our modeled world. The particles have position and speed, and they can migrate in the grid from cell to cell, depending on their motion model and motion parameters, but they will be also created and destroyed using a weighting-resampling mechanism that is specific to particle filtering algorithms. The tracking algorithm will be centered on particles, instead of cells. An obstacle grid derived from processing a stereovision-generated elevation map is used as measurement information, and the measurement model takes into account the uncertainties of the stereo reconstruction. The resulting system is a flexible real-time tracking solution for dynamic unstructured driving environments.

A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision

The urban driving environment is a complex and demanding one, requiring increasingly complex sensors for the driving assistance systems. These sensors must be able to analyze the complex scene and extract all the relevant information, while keeping the response time as low as possible. The sensor presented in this paper answers to the requirements of the urban scenario through a multitude of detection modules, built on top of a hybrid (hardware plus software) dense stereo reconstruction engine. The sensor is able to detect and track clothoid and non-clothoid lanes, cars, pedestrians (classified as such), and drivable areas in the absence of lane markings. The hybrid stereovision engine and the proposed detection algorithms allow accurate sensing of the demanding urban scenario at a high frame rate.

Accurate Ego-Vehicle Global Localization at Intersections Through Alignment of Visual Data With Digital Map

This paper proposes a method for achieving improved ego-vehicle global localization with respect to an approaching intersection, which is based on the alignment of visual landmarks perceived by the on-board visual system, with the information from a proposed extended digital map (EDM). The visual system relies on a stereovision system that provides a detailed 3-D description of the environment, including road landmark information (lateral lane delimiters, painted traffic signs, curbs, and stop lines) and dynamic environment information (other vehicles). An EDM is proposed, which enriches the standard map information with a detailed description of the intersection required for current lane identification, landmark alignment, and ego-vehicle accurate global localization. A novel approach for lane-delimiter classification, which is necessary for the lane identification, is also presented. An original solution for identifying the current lane, combining visual and map information with the help of a Bayesian network (BN), is proposed. Extensive experiments have been performed, and the results are evaluated with a Global Navigation Satellite System of high accuracy (2 cm). The achieved global localization accuracy is of submeter level, depending on the performance of the stereovision system.

Detection and classification of painted road objects for intersection assistance applications

For a Driving Assistance System dedicated to intersection safety, knowledge about the structure and position of the intersection is essential, and detecting the painted road signs can greatly improve this knowledge. This paper describes a method for detection, measurement and classification of painted road objects that are typically found in European intersections. The features of the painted objects are first extracted using dark light dark transition detection on horizontal line regions, and then are refined using gray level segmentation based on Gaussian mixtures. The 3D bounding box of the objects is reconstructed using perspective geometry. The objects are classified based on a restricted set of features, using a decision tree and size constraints.

Lane Geometry Estimation in Urban Environments Using a Stereovision System

This paper presents a lane detection system that combines stereovision-specific techniques with grayscale image processing for maximizing the robustness and applicability against the difficult conditions of the urban environment. The lane marking features are extracted using a fast and robust dark-light-dark transition detector thats aware of the perspective effect. The clothoid lane model is matched to the extracted features using line segment fitting for two distance intervals, under special constraints that ensure correctness. Freeform lane border detection, independent on the geometry constraints, driven by lane marking features only, is used to solve the situations not suited for clothoid representation. The results of each detection method are fused together in a Kalman filter based framework.

Increased Accuracy Stereo Approach for 3D Lane Detection

A new approach for the stereovision problem is presented, aiming to increase the accuracy of stereo reconstruction. The proposed method is edge-based and consists of the correlation of left and right contours, detected with sub-pixel accuracy. The steps of the stereo matching process are: segmentation of each contour into basic contours (strongly- and weakly-curved), detection of correspondences between left-right image basic contours, and computation with sub-pixel accuracy of the pairs of corresponding left-right edge points. By consequence, one achieves the highest correlation and 3D reconstruction accuracy. 3D lane detection, based on a clothoidal model, is evaluated with the proposed stereo algorithm on synthetic and real world images

Driving environment perception using stereovision

This paper presents a high accuracy, far range stereovision approach for driving environment perception based on 3D lane and obstacle detection. Stereovision allows the elimination of the common assumptions used in most monocular systems: flat road, constant pitch angle or absence of roll angle. The lane detection method is based on clothoidal 3D lane model. The detected lane parameters are the vertical and horizontal curvatures, the lane width and the roll angle. The detected lane profile is used for road obstacle features separation. Based on a vicinity criteria the over road 3D points are grouped and tracked over frames. The system detects and classifies the meaningful obstacles in terms of 3D position, size and speed.