This paper presents a closed-loop dynamic simulation-based design method for articulated heavy vehicles (AHVs) with active trailer steering (ATS) systems. AHVs have poor manoeuvrability at low speeds and exhibit low lateral stability at high speeds. From the design point of view, there exists a trade-off relationship between AHVs’ manoeuvrability and stability. For example, fewer articulation points and longer wheelbases will improve high-speed lateral stability, but they will degrade low-speed manoeuvrability. To tackle this conflicting design problem, a systematic method is proposed for the design of AHVs with ATS systems. In order to evaluate vehicle performance measures under a well-defined testing manoeuvre, a driver model is introduced and it ‘drivers’ the vehicle model to follow a prescribed route at a given speed. Considering the interactions between the mechanical trailer and the ATS system, the proposed design method simultaneously optimises the active design variables of the controllers and passive design variables of the trailer in a single design loop (SDL). Through the design optimisation of an ATS system for an AHV with a truck and a drawbar trailer combination, this SDL method is compared against a published two design loop method. The benchmark investigation shows that the former can determine better trade-off design solutions than those derived by the latter. This SDL method provides an effective approach to automatically implement the design synthesis of AHVs with ATS systems.

This paper provides valuable guidelines on the selection of dynamic vehicle models for control algorithm development, design optimization and linear stability analysis for multi-trailer articulated heavy vehicles with active safety systems. The validation of yaw-plane and yaw–roll models of a tractor–two-semitrailer combination using the TruckSim software package is presented in this paper. A linear four-degree-of-freedom yaw-plane model and a linear seven-degree-of-freedom yaw–roll model of the vehicle were generated, compared and evaluated. The linear models of the multi-trailer articulated heavy vehicle yield numerical simulation results which are validated by comparing with those obtained from the corresponding non-linear TruckSim model. This paper also includes eigenvalue and frequency-response analysis of the linear models to estimate the unstable motion modes and to predict the unique dynamic features of the multi-trailer articulated heavy vehicle in the frequency domain. A benchmark investigation of the models was performed to examine the fidelity, the complexity and the applicability of the linear models.

This paper presents the design for an active trailer-steering system of multi-trailer articulated heavy vehicles using driver-software-in-the-loop real-time simulations. A linear yaw-plane multi-trailer articulated heavy-vehicle model is generated to derive an optimal active trailer-steering controller. Then, the controller is reconstructed in LabVIEW and integrated with a vehicle model for a tractor– double-trailer combination developed in TruckSim. The driver-software-in-the-loop real-time simulations are conducted on a vehicle simulator. The driver-software-in-the-loop real-time simulations indicate that the active trailer-steering controller can effectively improve the low-speed manoeuvrability and high-speed stability of the multi-trailer articulated heavy vehicle when testing using a low-speed 360° roundabout path-following manoeuvre and a high-speed single-lane-change manoeuvre respectively. The investigation based on the driver-software-in-the-loop real-time simulations paves the way to future development of electronic control units for the active trailer-steering system using driver-hardware-in-the-loop real-time simulations.

The Society of Automotive Engineers issued a test procedure, SAE-J2179, to determine the rearward amplification (RA) of multi-trailer articulated heavy vehicles (MTAHVs). Built upon the procedure, the International Organization for Standardization released the test manoeuvres, ISO-14791, for evaluating directional performance of MTAHVs. For the RA measures, ISO-14791 recommends two single lane-change manoeuvres: (1) an open-loop procedure with a single sine-wave steering input; and (2) a closed-loop manoeuvre with a single sine-wave lateral acceleration input. For an articulated vehicle with active trailer steering (ATS), the RA measure in lateral acceleration under the open-loop manoeuvre was not in good agreement with that under the closed-loop manoeuvre. This observation motivates the research on the applicability of the two manoeuvres for the RA measures of MTAHVs with ATS. It is reported that transient response under the open-loop manoeuvre often leads to asymmetric curve of tractor lateral acceleration [Winkler CB, Fancher PS, Bareket Z, Bogard S, Johnson G, Karamihas S, Mink C. Heavy vehicle size and weight – test procedures for minimum safety performance standards. Final technical report, NHTSA, US DOT, contract DTNH22-87-D-17174, University of Michigan Transportation Research Institute, Report No. UMTRI-92-13; 1992]. To explore the effect of the transient response, a multiple cycle sine-wave steering input (MCSSI) manoeuvre is proposed. Simulation demonstrates that the steady-state RA measures of an MTAHV with and without ATS under the MCSSI manoeuvre are in excellent agreement with those under the closed-loop manoeuvre. It is indicated that between the two manoeuvres by ISO-14791, the closed-loop manoeuvre is more applicable for determining the RA measures of MTAHVs with ATS.

Multi-trailer articulated heavy vehicles (MTAHVs) are increasingly used around the world due to their economic and environmental benefits. However, MTAHVs exhibit poor maneuverability and low lateral stability, which may lead to fatal traffic accidents. Given the safety risks, it is necessary to solve the steering and stability problems of MTAHVs before they are safely mass deployed on our roads. To this end, active trailer steering (ATS) based on the linear quadratic regulator (LQR) technique has been explored. The LQR-based ATS demonstrates improved maneuverability and enhanced lateral stability. In the ATS design, the vehicle and operating parameters are assumed constant. Thus, it is natural to question the robustness of the ATS in presence of vehicle and operating parameter uncertainties. To address the problem, this paper proposes a robust ATS system. The robust ATS controller is designed using a linear matrix inequality (LMI) based LQR method. In the design, both vehicle and steering actuator parameter uncertainties are considered; to enhance the robustness of the ATS, the weighting matrices of the proposed controller are optimized. The robust controller is applied to an A-Train Double, one type of MTAHV. The effectiveness of the robust ATS is demonstrated using numerical and hardware-in-the-loop real-time simulations.

Autonomous vehicles face challenges in small-target detection and, in particular, in accurately identifying traffic lights under low visibility conditions, e.g., fog, rain, and blurred night-time lighting. To address these issues, this paper proposes an improved algorithm, namely KCS-YOLO (you only look once), to increase the accuracy of detecting and recognizing traffic lights under low visibility conditions. First, a comparison was made to assess different YOLO algorithms. The benchmark indicates that the YOLOv5n algorithm achieves the highest mean average precision (mAP) with fewer parameters. To enhance the capability for detecting small targets, the algorithm built upon YOLOv5n, namely KCS-YOLO, was developed using the K-means++ algorithm for clustering marked multi-dimensional target frames, embedding the convolutional block attention module (CBAM) attention mechanism, and constructing a small-target detection layer. Second, an image dataset of traffic lights was generated, which was preprocessed using the dark channel prior dehazing algorithm to enhance the proposed algorithm’s recognition capability and robustness. Finally, KCS-YOLO was evaluated through comparison and ablation experiments. The experimental results showed that the mAP of KCS-YOLO reaches 98.87%, an increase of 5.03% over its counterpart of YOLOv5n. This indicates that KCS-YOLO features high accuracy in object detection and recognition, thereby enhancing the capability of traffic light detection and recognition for autonomous vehicles in low visibility conditions.