Camera-Based Articulation Angle Sensing for Heavy Goods Vehicles

Articulation angle sensing is an essential component of manoeuvrability and stability control systems for articulated heavy goods vehicles, particularly long combination vehicles. Existing solutions to this sensing task are limited by reliance on trailer modifications or information or by measurement accuracy, or both, restricting commercial adoption. In this paper we present a purely tractor-based sensor concept comprising a rear-facing camera and the parallel tracking and mapping (PTAM) image processing algorithm. The system requires no prior knowledge of or modifications to the trailer, is compatible with planar and non-planar trailer shapes, and with multiply-articulated vehicle combinations. The system is validated in full-scale vehicle tests on both a tractor semi-trailer combination and a truck and full-trailer combination, demonstrating robust performance in a number of conditions, including trailers with non-planar geometry and with minimal visual features. Average RMS measurement errors of 1.19, 1.03 and 1.53 degrees were demonstrated for the semi-trailer and full-trailer (drawbar and semi-trailer) respectively. This compares favourably with the state-of-the-art in the published literature. A number of improvements are proposed for future development based on the observations in this research.This work was funded by the Cambridge Commonwealth, European and International Trust (CCEIT), the Council for Scientific and Industrial Research (CSIR, South Africa), and the Cambridge Vehicle Dynamics Consortium (CVDC). At the time of writing the Consortium consisted of the University of Cambridge with the following partners from the heavy vehicle industry: Anthony Best Dynamics, Camcon, Denby Transport, Firestone Industrial Products, Goodyear, Haldex, MIRA, SDC Trailers, Tinsley Bridge, Tridec, Volvo Trucks, and Wincanton

A path planning and path-following control framework for a general 2-trailer with a car-like tractor

Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice-based path planning framework is developed in order to generate kinematically feasible and collision-free paths and a path-following controller is designed to stabilize the lateral and angular path-following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed which only utilizes information from sensors that are mounted on the car-like tractor, making the system independent of additional trailer sensors. The proposed path planning and path-following control framework is implemented on a full-scale test vehicle and results from simulations and real-world experiments are presented.Comment: Preprin

Estimation of trailer off-tracking using visual odometry

High Capacity Vehicles (HCVs) have been shown to be highly effective in reducing emissions associated with road freight transport. However, the reduced manoeuvrability of long vehicles often necessitates the use of active trailer steering. Path-following trailer steering systems are very effective in this regard, but are currently limited to on-highway applications due to the manner in which trailer off-tracking is estimated. In this work, a novel trailer off- tracking measurement concept is introduced which is independent of wheel slip and ground surface conditions, and requires no additional sensor measurements or parameter data from the tractor. The concept utilises a stereo camera pair affixed to the trailer and a visual odometry-based algorithm to calculate off-tracking. The concept was evaluated in detailed simulation and full-scale vehicle tests, demonstrating its feasibility and highlighting some important characteristics. RMS measurement errors of 0.11-0.12 m (3.3-3.6%) were obtained in a challenging visual environment.CSIR, South Africa; Cambridge Commonwealth, European and International Trust, UK; Cambridge Vehicle Dynamics Consortium

Improving path-tracking performance of an articulated tractor-trailer system using a non-linear kinematic model

This paper presents a novel non-linear mathematical model of an articulated tractor-trailer system that can be used, in combination with receding horizon techniques, to improve the performance of path tracking tasks of articulated systems. Due to its dual steering mechanisms, this type of vehicle can be very useful in precision agriculture, particularly for seeding, spraying and harvesting in small fields. The articulated tractor-trailer system model was embedded within a non-linear model predictive controller and the trailer position was monitored. When the kinematic of the trailer was considered, the deviation of trailer's position was reduced substantially alongside not only straight paths but also in headland turns. Using the proposed mathematical model, we were able to control the trailer's position itself rather than the tractor's position. The Robot Operating System (ROS) framework and Gazebo simulator were used to perform realistic simulations examples.Fil: Murillo, Marina Hebe. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Sánchez, G.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Deniz, Nestor Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Genzelis, Lucas Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

Stability Control of Triple Trailer Vehicles

While vehicle stability control is a well-established technology in the passenger car realm, it is still an area of active research for commercial vehicles as indicated by the recent notice of proposed rulemaking on commercial vehicle stability by the National Highway Traffic Safety Administration (NHTSA, 2012). The reasons that commercial vehicle electronic stability control (ESC) development has lagged passenger vehicle ESC include the fact that the industry is generally slow to adopt new technologies and that commercial vehicles are far more complex requiring adaptation of existing technology. From the controller theory perspective, current commercial vehicle stability systems are generally passenger car based ESC systems that have been modified to manage additional brakes (axles). They do not monitor the entire vehicle nor do they manage the entire vehicle as a system

Vision-based trailer pose estimation for articulated vehicles

Articulated Heavy Goods Vehicles (HGVs) are more efficient than conventional rigid lorries, but exhibit reduced low-speed manoeuvrability and high-speed stability. Technologies such as autonomous reversing and path-following trailer steering can mitigate this, but practical limitations of the available sensing technologies restrict their commercialisation potential. This dissertation describes the development of practical vision-based articulation angle and trailer off-tracking sensing for HGVs. Chapter 1 provides a background and literature review, covering important vehicle technologies, existing commercial and experimental sensors for articulation angle and off-tracking measurement, and relevant vision-based technologies. This is followed by an introduction to pertinent computer vision theory and terminology in Chapter 2. Chapter 3 describes the development and simulation-based assessment of an articulation angle sensing concept. It utilises a rear-facing camera mounted behind the truck or tractor, and one of two proposed image processing methods: template-matching and Parallel Tracking and Mapping (PTAM). The PTAM-based method was shown to be the more accurate and versatile method in full-scale vehicle tests. RMS measurement errors of 0.4-1.6$^\circ$ were observed in tests on a tractor semi-trailer (Chapter 4), and 0.8-2.4$^\circ$ in tests on a Nordic combination with two articulation points (Chapter 5). The system requires no truck-trailer communication links or artificial markers, and is compatible with multiple trailer shapes, but was found to have increasing errors at higher articulation angles. Chapter 6 describes the development and simulation-based assessment of a trailer off-tracking sensing concept, which utilises a trailer-mounted stereo camera pair and visual odometry. The concept was evaluated in full-scale tests on a tractor semi-trailer combination in which camera location and stereo baseline were varied, presented in Chapter 7. RMS measurement errors of 0.11-0.13 m were obtained in some tests, but a sensitivity to camera alignment was discovered in others which negatively affected results. A very stiff stereo camera mount with a sub-0.5 m baseline is suggested for future experiments. A summary of the main conclusions, a review of the objectives, and recommendations for future work are given in Chapter 8. Recommendations include further refinement of both sensors, an investigation into lighting sensitivity, and alternative applications of the sensors.This work was supported by a "CSIR South Africa Cambridge Scholarship", funded jointly by the Cambridge Commonwealth, European & International Trust and the Council for Scientific & Industrial Research (CSIR South Africa)