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 new approach for the detection of the road surface and obstacles is presented. The high accuracy of the method allows the detection of traffic isles as a distinct class. The 3-D data inferred from dense stereo are transformed into a rectangular digital elevation map (DEM). Two classifiers are proposed, namely, density based and road surface based. The density-based obstacle classifier marks DEM cells as road or obstacles, using the density of 3-D points as a criterion. A quadratic road surface model is initially fitted by a random sample consensus (RANSAC) approach to the region in front of the ego vehicle. A region growing-like process refines this primary solution, driven by the 3-D uncertainty model of the stereo sensor. A robust global solution for the road surface is obtained. The road surface is used for discrimination between road, traffic isle, and obstacle points. Fusion and error filtering is performed on the results of the two classifiers. The proposed real-time algorithm was evaluated in an urban scenario and can be used in complex applications from collision avoidance to path planning.

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.

More data fidelity terms in variational optical flow methods improve the estimation's robustness. A robust and anisotropic smoother enhances the specific fill-in process. This work presents a combined local-global (CLG) approach with total variation regularization. The combination of bilateral filtering and anisotropic (image driven) regularization is used to control the propagation phenomena. The resulted method, CLG-TV, is able to compute larger displacements in a reasonable time. The numerical scheme is highly parallelizable and runs in real-time on current generation graphic processing units.

Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them. We present a new approach for standing- and walking-pedestrian detection, in urban traffic conditions, using grayscale stereo cameras mounted on board a vehicle. Our system uses pattern matching and motion for pedestrian detection. Both 2-D image intensity information and 3-D dense stereo information are used for classification. The 3-D data are used for effective pedestrian hypothesis generation, scale and depth estimation, and 2-D model selection. The scaled models are matched against the selected hypothesis using high-performance matching, based on the Chamfer distance. Kalman filtering is used to track detected pedestrians. A subsequent validation, based on the motion field's variance and periodicity of tracked walking pedestrians, is used to eliminate false positives.