Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

A New Approach To Estimate The Collision Probability For Automotive Applications

We revisit the computation of probability of collision in the context of automotive collision avoidance (the estimation of a potential collision is also referred to as conflict detection in other contexts). After reviewing existing approaches to the definition and computation of a collision probability we argue that the question "What is the probability of collision within the next three seconds?" can be answered on the basis of a collision probability rate. Using results on level crossings for vector stochastic processes we derive a general expression for the upper bound of the distribution of the collision probability rate. This expression is valid for arbitrary prediction models including process noise. We demonstrate in several examples that distributions obtained by large-scale Monte-Carlo simulations obey this bound and in many cases approximately saturate the bound. We derive an approximation for the distribution of the collision probability rate that can be computed on an embedded platform. In order to efficiently sample this probability rate distribution for determination of its characteristic shape an adaptive method to obtain the sampling points is proposed. An upper bound of the probability of collision is then obtained by one-dimensional numerical integration over the time period of interest. A straightforward application of this method applies to the collision of an extended object with a second point-like object. Using an abstraction of the second object by salient points of its boundary we propose an application of this method to two extended objects with arbitrary orientation. Finally, the distribution of the collision probability rate is identified as the distribution of the time-to-collision.Comment: Revised and restructured version, discussion of extended vehicles expanded, section on TTC expanded, references added, other minor changes, 17 pages, 18 figure

Predictive Maneuver Planning and Control of an Autonomous Vehicle in Multi-Vehicle Traffic with Observation Uncertainty

Autonomous vehicle technology is a promising development for improving the safety, efficiency and environmental impact of on-road transportation systems. However, the task of guiding an autonomous vehicle by rapidly and systematically accommodating the plethora of changing constraints, e.g. of avoiding multiple stationary and moving obstacles, obeying traffic rules, signals and so on as well as the uncertain state observation due to sensor imperfections, remains a major challenge. This dissertation attempts to address this challenge via designing a robust and efficient predictive motion planning framework that can generate the appropriate vehicle maneuvers (selecting and tracking specific lanes, and related speed references) as well as the constituent motion trajectories while considering the differential vehicle kinematics of the controlled vehicle and other constraints of operating in public traffic. The main framework combines a finite state machine (FSM)-based maneuver decision module with a model predictive control (MPC)-based trajectory planner. Based on the prediction of the traffic environment, reference speeds are assigned to each lane in accordance with the detection of objects during measurement update. The lane selection decisions themselves are then incorporated within the MPC optimization. The on-line maneuver/motion planning effort for autonomous vehicles in public traffic is a non-convex problem due to the multiple collision avoidance constraints with overlapping areas, lane boundaries, and nonlinear vehicle-road dynamics constraints. This dissertation proposes and derives some remedies for these challenges within the planning framework to improve the feasibility and optimality of the solution. Specifically, it introduces vehicle grouping notions and derives conservative and smooth algebraic models to describe the overlapped space of several individual infeasible spaces and help prevent the optimization from falling into undesired local minima. Furthermore, in certain situations, a forced objective selection strategy is needed and adopted to help the optimization jump out of local minima. Furthermore, the dissertation considers stochastic uncertainties prevalent in dynamic and complex traffic and incorporate them with in the predictive planning and control framework. To this end, Bayesian filters are implemented to estimate the uncertainties in object motions and then propagate them into the prediction horizon. Then, a pair-wise probabilistic collision condition is defined for objects with non-negligible geometrical shape/sizes and computationally efficient and conservative forms are derived to efficiently and analytically approximate the involved multi-variate integrals. The probabilistic collision evaluation is then applied within a vehicle grouping algorithms to cluster the object vehicles with closeness in positions and speeds and eventually within the stochastic predictive maneuver planner framework to tighten the chanced-constraints given a deterministic confidence margin. It is argued that these steps make the planning problem tractable for real-time implementation on autonomously controlled vehicles

Tracking of Spline Modeled Extended Objects Using Random Finite Sets

In the field of autonomously driving vehicles the environment perception containing dynamic objects like other road users is essential. Especially, detecting other vehicles in the road traffic using sensor data is of utmost importance. As the sensor data and the applied system model for the objects of interest are noise corrupted, a filter algorithm must be used to track moving objects. Using LIDAR sensors one object gives rise to more than one measurement per time step and is therefore called extended object. This allows to jointly estimate the objects, position, as well as its orientation, extension and shape. Estimating an arbitrary shaped object comes with a higher computational effort than estimating the shape of an object that can be approximated using a basic geometrical shape like an ellipse or a rectangle. In the case of a vehicle, assuming a rectangular shape is an accurate assumption. A recently developed approach models the contour of a vehicle as periodic B-spline function. This representation is an easy to use tool, as the contour can be specified by some basis points in Cartesian coordinates. Also rotating, scaling and moving the contour is easy to handle using a spline contour. This contour model can be used to develop a measurement model for extended objects, that can be integrated into a tracking filter. Another approach modeling the shape of a vehicle is the so-called bounding box that represents the shape as rectangle. In this thesis the basics of single, multi and extended object tracking, as well as the basics of B-spline functions are addressed. Afterwards, the spline measurement model is established in detail and integrated into an extended Kalman filter to track a single extended object. An implementation of the resulting algorithm is compared with the rectangular shape estimator. The implementation of the rectangular shape estimator is provided. The comparison is done using long-term considerations with Monte Carlo simulations and by analyzing the results of a single run. Therefore, both algorithms are applied to the same measurements. The measurements are generated using an artificial LIDAR sensor in a simulation environment. In a real-world tracking scenario detecting several extended objects and measurements that do not originate from a real object, named clutter measurements, is possible. Also, the sudden appearance and disappearance of an object is possible. A filter framework investigated in recent years that can handle tracking multiple objects in a cluttered environment is a random finite set based approach. The idea of random finite sets and its use in a tracking filter is recapped in this thesis. Afterwards, the spline measurement model is included in a multi extended object tracking framework. An implementation of the resulting filter is investigated in a long-term consideration using Monte Carlo simulations and by analyzing the results of a single run. The multi extended object filter is also applied to artificial LIDAR measurements generated in a simulation environment. The results of comparing the spline based and rectangular based extended object trackers show a more stable performance of the spline extended object tracker. Also, some problems that have to be addressed in future works are discussed. The investigation of the resulting multi extended object tracker shows a successful integration of the spline measurement model in a multi extended object tracker. Also, with these results some problems remain, that have to be solved in future works

Data association and occlusion handling for vision-based people tracking by mobile robots

This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets