In this paper, we introduce a novel method for the estimation of vehicle pose and extent in traffic surveillance scenarios based on camera data. The state estimation is performed in a common world frame, enabling the seamless integration of the image data from different viewpoints. Our approach incorporates the non-linear transformation between the measurements and the states directly into the framework of an Unscented Kalman filter. Two measurement models are proposed: one designed for bounding boxes and another for discretized object contours extracted from segmentation masks. The method is evaluated using data from a real-world traffic surveillance system, demonstrating the high effectiveness and good feasibility of our approach for localizing passing cars.

In this paper, we present a novel design for a multi-camera tracking system with occlusion-handling capabilities and its application to a highway traffic surveillance system. The fundamental concept follows the tracking-by-detection principle with monocular detectors and an LMB tracker for tracking the objects in the world frame. All data from the multi-view setup is combined into one consistent representation of the real-time traffic situation. In order to assess the inter-target occlusion scenarios in 3D, the vehicles are modeled as cuboids and their extents are estimated from the bounding boxes provided by the detectors. We re-transform the 3D occlusion estimation problem into the 2D camera space and present two methods for quantifying the occlusion state of the objects. Moreover, we propose a modification to the computation of the existence probability of undetected and occluded targets. Based on this, the tracking system is extended by an occlusion-aware detection model. We evaluate our occlusion-handling approach on a real-world traffic dataset from the Providentia++ project and show an improved tracking performance. We find that the number of misdetected targets is reduced and more track identities are preserved.

One of the primary challenges for a successful Highly Assisted and/or Autonomous Vehicle is its localization. To improve the precision of location of the vehicle, not only the internal sensors are being used, but also using data from external sensors is attracting increasing attention from the research community. One such proposed sensor is an infrastructure RADAR which can be used to improve the localization of the ego-vehicle. Although a RADAR indeed is a supplementary source of information, it suffers a unique type of clutter which have trajectories like real objects and can therefore result in “ghost measurements”, i.e., measurements which do not correspond to any real vehicles. This deteriorates the quality of the fused state estimates. This paper proposes a robust method to fuse the RADAR readings in presence of such outliers. This methodology builds upon a previously proposed solution where the problem was formulated as a factor graph. The RADAR measurements were added as a novel constraint of sum of inter-vehicle distance, called Topology Factor. Our previous work assumed clutter free environment. This paper proposes a novel robust Topology Factor which is also resilient against above mentioned outliers. Simulations (based on real data) show promising results in the direction of lowering the degradation of fused state estimates in presence of such clutter.

In this paper, we report on a body state and ground profile estimator for a snake-like robot executing a rolling gait to travel from flat ground to a slope. With the help of the estimator, the snake-like robot can adaptively adjust the body shape and locomotion speed by changing the gait parameters for the purpose of tackling a steep slope. Specifically, we propose a repeating sequence of continuous time dynamical models to fuse kinematic encoder data with on-board Inertial Measurement Unit (IMU) measurements based on extended Kalman filter (EKF). All the sensors are mounted inside each module of the snake-like robot, which measure the joint position, the three-axis acceleration, and the three-axis angular velocity. Further, the robot changes its moving pattern under our policy, judging by the estimated angle of the ground profile. We implement this estimation procedure off-line, using data extracted from repeated runs of the snake-like robot by simulation and evaluate its performance compared to the ground truth.

This paper presents a novel and an improved approach for estimating the position of a vehicle using vehicle-infrastructure cooperative localization. In our previous work we presented a Factor Graph based solution which added the topology (inter-vehicle distance) as a constraint while localizing the vehicle using data from sensors from both inside and outside the vehicle. This paper extends the work by reducing the error in calculating the precision of the position by almost 27% in the best case and lowering the computational time by at least 50% over our previously proposed solution. This is achieved by modifying current topology constraints to be also dependent on the previous state estimate. The proposed solution remains scalable for many vehicles without increasing the execution complexity. Finally, simulations indicate that incorporating the new topology information via Factor Graphs can improve performance over the traditional, state of the art, Kalman Filter approach.

Autonomous vehicles operating in dynamic environments rely on precise localization. In this paper we present a novel approach for cooperative localization of vehicular systems and an infrastructure RADAR which is resilient against outliers generated from the RADAR. The problem of cooperative localization is represented as a factor graph, where interrelated topologies (including that of outliers) are added as constraint factor between vehicle states. Corresponding probabilities for multiple topologies between states of the two vehicles are calculated using the Probability Data Association Filter and assigned to the respective edges in the graph. Simulation results indicate that this technique has significant benefits in the context of improving the resilience against outliers while optimizing joint state estimates. The methodology presented in this paper has the potential to provide a robust and flexible framework for cooperative localization in the presence of clutter, obscuration and targets entering and leaving the field of view.

In this paper we present an approach to designing a novel type of fuzzy controller. B-spline basis functions are used for input variables and fuzzy singletons for output variables to specify linguistic terms. “Product” is chosen as the fuzzy conjunction, and “centroid” as the defuzzification method. By appropriately designing the rule base, a fuzzy controller can be interpreted as a B-spline interpolator. Such a fuzzy controller may learn to approximate any known data sequences and to minimize a certain cost function. By choosing such a function appropriately, the learning process can be made to converge rapidly. We applied this approach to the problems of function approximation and both supervised and unsupervised learning of mobile robots. Experiments validate the advantages of this approach. © 1998 John Wiley & Sons, Inc.13: 257–285, 1998