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Abdeljalil Abbas-Turki

Publications and source records attributed to Abdeljalil Abbas-Turki.

3 recordsLinked to original sources

A Novel Path-Tracking Algorithm for Automated Tractor-Trailer Forward and Backward Maneuvers

Fully autonomous tractor--trailer systems are increasingly deployed in logistics, agriculture, and industrial environments, where precise and robust path-tracking capabilities are essential. However, the articulation between the tractor and the trailer introduces additional nonlinearities and significantly complicates lateral and longitudinal control, particularly during reversing maneuvers. This paper introduces a novel path-tracking algorithm specifically designed for articulated vehicles with a single trailer. The proposed method combines a lateral control law applied at the trailer level with a short-horizon predictive adjustment of the tractor steering angle, ensuring stable convergence toward the desired path in both forward and backward motion. The approach is geometry-based and requires no per-vehicle calibration or training. Simulation studies in a high-fidelity physics simulator demonstrate the ability of the controller to match or outperform classical and state-of-the-art methods in terms of accuracy, stability, and robustness to disturbances.

cs.RO↗

Path Tracking with Dynamic Control Point Blending for Autonomous Vehicles: An Experimental Study

This paper presents an experimental study of a path-tracking framework for autonomous vehicles in which the lateral control command is applied to a dynamic control point along the wheelbase. Instead of enforcing a fixed reference at either the front or rear axle, the proposed method continuously interpolates between both, enabling smooth adaptation across driving contexts, including low-speed maneuvers and reverse motion. The lateral steering command is obtained by barycentric blending of two complementary controllers: a front-axle Stanley formulation and a rear-axle curvature-based geometric controller, yielding continuous transitions in steering behavior and improved tracking stability. In addition, we introduce a curvature-aware longitudinal control strategy based on virtual track borders and ray-tracing, which converts upcoming geometric constraints into a virtual obstacle distance and regulates speed accordingly. The complete approach is implemented in a unified control stack and validated in simulation and on a real autonomous vehicle equipped with GPS-RTK, radar, odometry, and IMU. The results in closed-loop tracking and backward maneuvers show improved trajectory accuracy, smoother steering profiles, and increased adaptability compared to fixed control-point baselines.

cs.RO↗

AUTOPSY: A Framework for Tackling Privacy Challenges in the Automotive Industry

With the General Data Protection Regulation (GDPR) in place, all domains have to ensure compliance with privacy legislation. However, compliance does not necessarily result in a privacy-friendly system as for example getting users' consent to process their data does not improve the privacy-friendliness of the system. Therefore, the goal of the AUTOPSY project was to support the privacy engineering process in the automotive domain by providing several building blocks which technically improve the privacy-friendliness of modern, i.e., connected and (partially) automated vehicles. This paper presents the results of the AUTOPSY project: a system model to identify relevant entities and locations to apply privacy enhancing technologies (PETs); the privacy manager aiming at more control of the data flow from the vehicle, a PET selection approach based on GDPR principles, and an architectural framework for automotive privacy. Furthermore, we built a demonstrator for location-based services to evaluate the architectural framework.

cs.CR↗