We present a vehicle system capable of navigating safely and efficiently around Vulnerable Road Users (VRUs), such as pedestrians and cyclists. The system comprises key modules for environment perception, localization and mapping, motion planning, and control, integrated into a prototype vehicle. A key innovation is a motion planner based on Topology-driven Model Predictive Control (T-MPC). The guidance layer generates multiple trajectories in parallel, each representing a distinct strategy for obstacle avoidance or non-passing. The underlying trajectory optimization constrains the joint probability of collision with VRUs under generic uncertainties. To address extraordinary situations ('edge cases') that go beyond the autonomous capabilities - such as construction zones or encounters with emergency responders - the system includes an option for remote human operation, supported by visual and haptic guidance. In simulation, our motion planner outperforms three baseline approaches in terms of safety and efficiency. We also demonstrate the full system in prototype vehicle tests on a closed track, both in autonomous and remotely operated modes.

Ensuring safety remains one of the biggest challenges for the widespread adoption of automated vehicles (AVs). Remote operation of AVs is a promising approach to address this, allowing remote operators to intervene when AVs encounter edge cases. However, remote operators are out-of-the-loop from the conventional driver in vehicle environment interaction, impacting their situation awareness and ability to safely control or assist the vehicle. In the scenario of remote driving, this is more evident since multimodal feedback is required to replicate the conventional driver-vehicle environment-interaction. In addition to visual and auditory modalities, motion feedback has been proposed as a way to bridge the gap between remote driving and in-vehicle driving. However, since motion feedback is cost-intensive, it might hinder rapid upscaling of remote driving systems. Thus, this study evaluated whether motion feedback adds value to driving performance and experience of the remote operator in low-velocity scenarios. Driving performance and experience were assessed and compared using objective and subjective metrics in three conditions (in-vehicle driving, and remote driving with and without motion feedback). The findings show that in remote driving, motion feedback fails to provide significant improvements. When compared to in-vehicle driving, remote driving performance and experience remain significantly worse. This suggests that motion feedback, in its current form, is redundant in low-velocity scenarios and that a simplified Remote Driving Station (RDS) may be sufficient in these scenarios. Future work should optimize simplified RDS designs, enhance feedback and human-machine interfaces and explore different driving scenarios for safe and efficient remote driving.

A plausible explanation about the acquisition and realization of beliefs by the central nervous system (CNS) when issuing control actions to counteract external perturbations, is to employ mechanisms aiming to minimize sensory conflict and muscle effort while maintaining biomechanical stability. However, existing head–neck postural control models fail to explicitly integrate this plausible CNS objective within their stabilization mechanisms. This study proposes a novel Model Predictive Control (MPC)-based framework to replicate CNS postural stabilization by incorporating the minimization of sensory conflict as a primary control objective through the MPC cost function. The MPC is integrated in a simplified biomechanical head–neck structure, using a prediction model and sensory feedback to optimize control actions over a finite time horizon within biomechanical constraints. Two human experiments measuring head motion with unpredictable seat and trunk perturbations were used to evaluate and validate different configurations of sensory feedback pathways. During anterior–posterior translational trunk perturbations, the results illustrated that the configuration with vestibular feedback improved head position prediction while muscle effort and partial somatosensory feedback alone, achieved superior results in head pitch prediction. Meanwhile, muscle effort and partial somatosensory feedback were sufficient to stabilize the head during trunk rotational (pitch) perturbations. Finally, a multi-scenario optimization demonstrated that a single set of MPC weights could generalize stabilization across both perturbation types. The results demonstrate the effectiveness of MPC in replicating CNS-inspired postural adjustments, indicating that controlling a simplified biomechanical head–neck model provides a computationally efficient and accurate alternative to complex multi-segment approaches.

The goal of this paper is to contribute to the accurate prediction of human body motion by proposing a novel head-neck model for dynamic driving scenarios with complex vehicle motions. While automated vehicles are considered a potential solution to several transportation issues, there are still significant challenges that need to be addressed, including fundamental questions regarding motion comfort and postural stability. Existing standards fail to accurately describe motion comfort, and current head-neck models have limitations, such as their inability to accurately capture human head responses to dynamic perturbations and lack of adaptability to different perturbations, amplitudes, and individual characteristics. To address these challenges, the authors propose a 3D double inverted pendulum model (DIPM) with a total of 6 degrees of freedom (DoF) as an approximation of head-neck system. The proposed model uses Model Predictive Control (MPC) to derive optimal control inputs for head-neck stabilization. The study validates the proposed model against experimental data of anterior-posterior seat translation and rotation from the literature. The results indicate that the model fitted the experimental data with a variance accounted for 82.80 % in translation and 73.15 % in rotation (pitch). The proposed model paves the path for the accurate assessment of occupants’ postural stability in automated vehicles.