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This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline.

Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms.

Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles.

To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.

Despite South Africa’s ailing electrical grid, substantial renewable energy (RE) integration is planned for the country. As grid-integrated RE affects all grids differently, this study aims to develop an adaptable grid code-guided renewable power plant (RPP) control real-time simulation testbed, tailored to South African grid code requirements to study grid-integrated RE’s behaviour concerning South Africa’s unique conditions.

The testbed is designed using MATLAB’s Simulink and live script environments, to create an adaptable model where grid, RPP and RPP guiding grid codes are tailorable. This model is integrated with OPAL-RT’s RT-LAB and brought to real-time simulation using OPAL-RT’s OP4510 simulator. Voltage, frequency and short-circuit event case studies are performed through which the testbed’s abilities and performance are assessed.

Case study results show the following. The testbed accurately represents grid code voltage and frequency requirements. RPP point of connection (POC) conditions are consistently recognized and tracked, according to which the testbed then operates simulated RPPs, validating its design. Short-circuit event simulations show the simulated wind farm supports POC conditions relative to short-circuit intensity by curtailing active power in favour of reactive power, in line with local grid code requirements.

To the best of the authors’ knowledge, this is the first design of an adaptable grid code-guided RPP control testbed, tailored to South African grid code requirements in line with which RPP behavioural and grid integration studies can be performed.