In last decades, great technology advances have been done related to the automotive sector, especially in Advanced Driver Assistance Systems (ADAS) developed to improve mobility in terms of comfort and safety during driving process; hence, automated driving is presented as an evolution of those systems in the present and upcoming years. The aim of this work is to present a complete framework of motion planning for automated vehicles, considering different constraints with parametric curves for lateral and longitudinal planners. Parametric Bézier curves are used as the core approach for trajectory design in intersections, roundabouts, and lane change maneuvers. Additionally, a speed planner algorithm is presented using the same parametric curve approach, considering comfort and safety. A simulation environment is used for testing the planning method in urban conditions. Finally, tests with the real platform in automated mode have been performed showing goods results.

The low-speed high automation (LSHA) is foreseen as a development path for new types of mobility, improving road safety and addressing transit problems in urban infrastructures. As these automation approaches are still in the development phase, methods to improve their design and validation are required. The use of vehicle simulation models allows reducing significantly the time deployment on real test tracks, which would not consider all the scenarios or complexity related to automated driving features. However, to ensure safety and accuracy while evaluating the proper operation of LSHA features, adequate validation methodologies are mandatory. In this study a two-step validation methodology is proposed: Firstly, an open-loop test set attempts to tune the required vehicle simulation models using experimental data considering also the dynamics of the actuation devices required for vehicle automation. Secondly, a closed-loop test strives to validate the selected automated driving functionality based on test plans, also improving the vehicle dynamics response. To illustrate the methodology, a study case is proposed using an automated Renault Twizy. In the first step, the brake pedal and steering wheel actuators’ behavior is modeled, as well as its longitudinal dynamics and turning capacity. Then, in a second step, an LSHA functionality for Traffic Jam Assist based on a Model Predictive Control approach is evaluated and validated. Results demonstrate that the proposed methodology is capable not only to tune vehicle simulation models for automated driving development purposes but also to validate LSHA functionalities.

Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger’s safety and comfort during the fall-back maneuvering execution.

Achieving robust path tracking is essential for efficiently operating autonomous driving systems, particularly in unpredictable environments. This paper introduces a novel path-tracking control methodology utilizing a variable second-order Sliding Mode Control (SMC) approach. The proposed control strategy addresses the challenges posed by uncertainties and disturbances by reconfiguring and expanding the state-space matrix of a kinematic bicycle model guaranteeing Lyapunov stability and convergence of the system. A state prediction is integrated into the developed SMC to mitigate response time delays. Furthermore, the controller integrates adaptive mechanisms to adjust time-varying parameters within the control formulation based on longitudinal velocity, thereby enhancing path-tracking performance and reducing chattering phenomena. The effectiveness of the proposed approach is comprehensively evaluated through simulations and experiments encompassing challenging driving scenarios characterized by high-curvature paths, varying altitudes, and sensor disturbances, typical in rural driving environments. Results demonstrate that disturbances have varying impacts depending on the type of sensor affected. Real-world tests validate these findings, offering practical insights for automated vehicle path-tracking implementation.