TScan: Stationary LiDAR for Traffic and Safety Studies—Object Detection and Tracking

The ability to accurately measure and cost-effectively collect traffic data at road intersections is needed to improve their safety and operations. This study investigates the feasibility of using laser ranging technology (LiDAR) for this purpose. The proposed technology does not experience some of the problems of the current video-based technology but less expensive low-end sensors have limited density of points where measurements are collected that may bring new challenges. A novel LiDAR-based portable traffic scanner (TScan) is introduced in this report to detect and track various types of road users (e.g., trucks, cars, pedestrians, and bicycles). The scope of this study included the development of a signal processing algorithm and a user interface, their implementation on a TScan research unit, and evaluation of the unit performance to confirm its practicality for safety and traffic engineering applications. The TScan research unit was developed by integrating a Velodyne HDL-64E laser scanner within the existing Purdue University Mobile Traffic Laboratory which has a telescoping mast, video cameras, a computer, and an internal communications network. The low-end LiDAR sensor’s limited resolution of data points was further reduced by the distance, the light beam absorption on dark objects, and the reflection away from the sensor on oblique surfaces. The motion of the LiDAR sensor located at the top of the mast caused by wind and passing vehicles was accounted for with the readings from an inertial sensor atop the LiDAR. These challenges increased the need for an effective signal processing method to extract the maximum useful information. The developed TScan method identifies and extracts the background with a method applied in both the spherical and orthogonal coordinates. The moving objects are detected by clustering them; then the data points are tracked, first as clusters and then as rectangles fit to these clusters. After tracking, the individual moving objects are classified in categories, such as heavy and non-heavy vehicles, bicycles, and pedestrians. The resulting trajectories of the moving objects are stored for future processing with engineering applications. The developed signal-processing algorithm is supplemented with a convenient user interface for setting and running and inspecting the results during and after the data collection. In addition, one engineering application was developed in this study for counting moving objects at intersections. Another existing application, the Surrogate Safety Analysis Model (SSAM), was interfaced with the TScan method to allow extracting traffic conflicts and collisions from the TScan results. A user manual also was developed to explain the operation of the system and the application of the two engineering applications. Experimentation with the computational load and execution speed of the algorithm implemented on the MATLAB platform indicated that the use of a standard GPU for processing would permit real-time running of the algorithms during data collection. Thus, the post-processing phase of this method is less time consuming and more practical. Evaluation of the TScan performance was evaluated by comparing to the best available method: video frame-by-frame analysis with human observers. The results comparison included counting moving objects; estimating the positions of the objects, their speed, and direction of travel; and counting interactions between moving objects. The evaluation indicated that the benchmark method measured the vehicle positions and speeds at the accuracy comparable to the TScan performance. It was concluded that the TScan performance is sufficient for measuring traffic volumes, speeds, classifications, and traffic conflicts. The traffic interactions extracted by SSAM required automatic post-processing to eliminate vehicle interactions at too low speed and between pedestrians – events that could not be recognized by SSAM. It should be stressed that this post processing does not require human involvement. Nighttime conditions, light rain, and fog did not reduce the quality of the results. Several improvements of this new method are recommended and discussed in this report. The recommendations include implementing two TScan units at large intersections and adding the ability to collect traffic signal indications during data collection

Control of Formation Flight Via Extremum Seeking

We present a comprehensive design procedure based on extremum seeking for minimum power demand formation flight, the first with performance guarantees. The procedure involves the design of a new wake robust formation hold autopilot, and transformation of the closed loop aircraft dynamics to a form in which a newly available rigorous design procedure for extremum. seeking is applicable. We apply the design procedure on a formation of Lockheed C-5s, extending the use of maximum performance formation flight to large transports. Using available experimental wake data of the C-5, we develop a model of the aircraft in the wake that models aerodynamic interference as feedback nonlinearities. Thus, our work is also the first to attain stable extremum. seeking for a plant with nonlinear feedback. Optimal formation flight is attained by online minimization of an easily measurable objective, the pitch angle of the wingman

Equivalence between Neighboring-Extremal Control and Self-Optimizing Control for the Steady-State Optimization of Dynamical Systems

The problem of steering a dynamical system toward optimal steady-state performance is considered. For this purpose, a static optimization problem can be formulated and solved. However, because of uncertainty, the optimal steady-state inputs can rarely be applied directly in an open-loop manner. Instead, plant measurements are typically used to help reach the plant optimum. This paper investigates the use of optimizing control techniques for input adaptation. Two apparently different techniques of enforcing steady-state optimality are discussed, namely, neighboring-extremal control and self-optimizing control based on the null-space method. These two techniques are compared for the case of unconstrained real-time optimization in the presence of parametric variations. It is shown that, in the noise-free scenario, the two methods can be made equivalent through appropriate tuning. Note that both approaches can use measurements that are taken either at successive steady-state operating points or during the transient behavior of the plant. Implementation of optimizing control is illustrated through a simulated CSTR example