Robust Articulated Intelligent Driving for Enhanced Reversing -- RAIDER

Research Project, 2025 – 2028

The RAIDER project is organized into six work packages: project administration, requirements and conditions, modeling and simulation, estimation and control, validation, and dissemination. It spans from 2025 to 2028, with milestones including scenario definition, validated models, tested control strategies, and a PhD defense. The project involves Chalmers and Volvo, combining academic research with industrial testing, and is funded primarily through the FFI Safe Automated Driving program.

Reversing articulated vehicles, especially those with multiple trailers, is a complex and risk-prone task in logistics operations. Longer vehicle combinations are increasingly used for efficiency and sustainability, but they introduce significant challenges during low-speed maneuvers like shunting. Current solutions, such as cameras and sensors, provide limited assistance, and existing automated systems for passenger cars are not directly applicable to heavy-duty vehicles. There is a clear need for robust, real-world solutions that reduce driver workload and improve safety.

The project aims to develop intelligent reversing aids for multi-articulated vehicles that are robust across diverse conditions and configurations. Key goals include improving maneuverability, reducing operational risks, and enabling efficient use of space in logistics hubs. The system should be combination-agnostic, scalable, and adaptable to variations in geometry, coupling types, and environmental factors. Ultimately, the project supports safer, more sustainable transport and facilitates the adoption of high-capacity transport solutions.

The approach combines scenario analysis, dynamic modeling, and advanced estimation and control strategies. Work begins with identifying real-world conditions and sensor setups, followed by creating simulation models to capture variations. Estimation techniques (e.g., Kalman filters) and robust control algorithms will be developed to handle uncertainties. Validation will occur through systematic testing on instrumented trucks and trailers under varied conditions. Dissemination includes academic publications and a PhD thesis, ensuring knowledge transfer and industry impact.

The project is expected to deliver methods for adaptive, context-aware shunting aids that significantly reduce reversing complexity and time. Potential outcomes include improved safety, operational efficiency, and reduced environmental impact through better logistics flow and support for high-capacity transport.