Fully autonomous driving has long been promised, yet remains difficult to deploy at scale. Safe and efficient autonomy hinges on motion planning and control under tight constraints, realistic vehicle dynamics, and reliable cooperation between vehicles, often in the presence of delays, uncertainty, and adversarial communication. Progress in these areas depends on fast design–test cycles, but current validation workflows are polarized, high-fidelity simulation enables rapid iteration, yet struggles to reproduce road–tire interaction, actuator and sensor dynamics, and multi-agent network effects. Full-scale experiments capture these effects, but are costly, slow, and constrained by safety and regulatory requirements. This thesis addresses this gap by enabling physically grounded experimentation by designing a low cost and reproducible robotic platform, and by developing control and coordination methods for cooperative driving applications for more efficient traffic flow, as well as model based control strategies to handle safety critical maneuvers at the limits of handling.

Three limitations in the state of the art motivate the research objectives of this thesis. First, existing small-scale car-like platforms either remain relatively expensive for multi-agent experiments, depend on custom components that hinder reproducibility, or lack rigorous system-identification workflows needed for model-based control and sim-to-hardware transfer. Second, cooperative driving methods, for example platooning, often rely on timely attack detection or hard switching to degraded modes, leaving vulnerability windows and risking undesirable transients, moreover, adversarial robustness at the coordination and topology-management level, such as merge, split, rearrange, is less developed than low-level spacing control. Third, widely used MPCC formulations can become unreliable on highly curved paths because internal progress approximations drift under large deviations, which can degrade constraint handling, racing-oriented MPC variants frequently depend on offline priors, for example precomputed racelines, limiting transfer to emergency-like maneuvers where only local geometry is available. Accordingly, this thesis aims to develop: (I) a reproducible, low-cost experimental platform with a complete identification pipeline, (II) cooperative driving algorithms that preserve safety even under persistent or undetected communication attacks and remain feasible under actuator saturation, and (III) curvature-aware MPCC formulations that remain reliable on tight curvature and avoid dependence on offline priors in high-performance settings.

The thesis begins with the design and characterization of the Delft Autonomous-driving Robotic Testbed (DART). DART is a low-cost, reproducible, small-scale vehicle built on a commercial RC chassis and augmented with additional sensing and computational capabilities, including Lidar, an IMU, custom wheel encoders, and onboard computing. The platform preserves the essential features of full-scale vehicle dynamics, Ackermann steering, suspensions, electric motor, while remaining small enough to work with in laboratory settings. A central component is a comprehensive system identification procedure that yields reliable kinematic and vehicle dynamics models tailored to small-scale vehicles, along with sub-models for motor force, friction, steering actuation, and tyre lateral forces. This modelling pipeline enables realistic testing and accurate model-based control on hardware.

The thesis then shifts focus to cooperative multi-vehicle systems by introducing a distributed, attack-resilient platooning framework. This work addresses two intertwined challenges: maintaining safety and formation integrity under malicious interference on the communication channels, and enabling coordinated platoon-level decisions such as merging, splitting, and rearranging. At the control level, the method combines sensor-based Adaptive Cruise Control with communication-based Cooperative Adaptive Cruise Control, while a safety filter ensures collision avoidance even when communicated acceleration data is corrupted. At the coordination level, a distributed topology-management strategy detects inconsistent information and isolates compromised vehicles by reorganizing the platoon. The approach is validated in simulation and experimentally on multiple DART vehicles, showing that safety and string stability are preserved despite communication attacks.

With this platform at hand, we then focus on designing a motion-planning strategy for urban driving. We investigate model predictive control and develop a Curvature-Aware Model Predictive Contouring Control (CA-MPCC) framework. Traditional MPCC formulations assume low curvature and rely on a lag-error term that couples progression along the reference path with lateral tracking. This complicates tuning and reduces reliability in tight curves. The CA-MPCC formulation resolves these limitations by explicitly accounting for curvature in the path geometry, removing the lag-error term entirely and simplifying both the cost structure and the tuning process. The method is validated in simulation and on the DART platform, demonstrating improved robustness, reduced parameter sensitivity, and reliable real-time performance even on highly curved trajectories.

Next, the thesis extends the curvature-aware MPCC methodology to high-performance domains with the rCA-MPCC framework for autonomous racing. Racing introduces additional challenges, such as operating at the limits of handling, rapid curvature changes, and strong coupling between dynamical states. The rCA-MPCC formulation augments CA-MPCC with a curvature-informed terminal cost and a compact reference-path representation that avoids dependence on precomputed race line information. This chapter also expands the physical vehicle model through actuator-dynamics identification and residual dynamic modeling using Gaussian Processes. Together, these advances improve prediction accuracy and enable robust high-speed control. We furthermore extend the car-racing formulation to aerial drone racing, bridging the gap between the two communities. Experiments on small-scale cars and simulations on quadrotor drones demonstrate faster, more consistent lap times and robustness across different dynamic model choices.

Finally, the thesis demonstrates how the developed platform and control tools can be applied to cooperative multi-robot tasks such as persistent monitoring and target detection. The chapter combines DART with the CA-MPCC controller to implement Lissajous-curve-based coordinated trajectories generated by time-inverted Kuramoto dynamics. The result is a distributed coordination strategy that guarantees collision avoidance and complete area coverage. Experimental validation confirms that the high-level coordination method and low-level CA-MPCC controller integrate smoothly on real hardware, even under disturbances and temporary agent failures.

Overall, this thesis contributes a unified framework for physically grounded experimentation, accurate dynamics modeling, and robust control and coordination in autonomous driving. By developing a reproducible platform, constructing reliable models, and demonstrating advanced control strategies, from emergency maneuvers to cooperative platooning and multi-agent monitoring, it lowers the barrier to real-world validation and accelerates iteration cycles for autonomous driving research. The insights presented here support future work toward safer, more robust, and more efficient autonomous transportation systems.

This paper presents a novel distributed vehicle platooning control and coordination strategy. We propose a distributed predecessor-follower CACC scheme that allows to choose an arbitrarily small inter-vehicle distance while guaranteeing no rear-end collisions occur, even in the presence of undetected cyber-attacks on the communication channels such as false data injection. The safety guarantees of the CACC policy are derived by combining a sensor-based ACC policy that explicitly accounts for actuator saturation, and a communication-based predictive term that has state-dependent limits on its control authority, thus containing the effects of an unreliable communication channel. An undetected attack may still however be able to degrade platooning performance. To mitigate it, we propose a tailored Kalman observer-based attack detection algorithm that initially triggers a switch from the CACC policy to the ACC policy. Subsequently, by relying on a high-level coordinator, our strategy allows to isolate a compromised vehicle from the platoon formation by reconfiguring the platoon topology itself. The coordinator can also handle merging and splitting requests. We compare our algorithm in an extensive simulation study against a state of the art distributed MPC scheme and a robust control scheme. We additionally extensively test our full method in practice on a real system, a team of scaled-down car-like robots. Furthermore, we share the code to run both the simulations and robotic experiments.

We propose a novel approach to track the state of charge (SoC) of batteries in mobile robots to improve their capabilities. The batteries' status is critical to accomplish their mission, but limited battery life can be a challenge. Our methodology focuses on modeling and estimating the SoC of batteries through system identification and fractional-order models. These models are flexible and can adjust to transient responses, allowing for accurate estimation of battery characteristics. Specifically, we use cubic spline interpolation to obtain the open-circuit voltage (OCV) and the different resistors of the battery model. To estimate the SoC, we deploy a novel approach based on the moving horizon estimation (MHE) algorithm, which is suitable for handling poor initial estimation and constraints on the battery model. We consider the constraint on the peak discharging current, which can limit the performance of mobile robots in low-battery mode. We validate our approach by applying system identification and MHE to data from a mobile robot. The results show that our method accurately estimates the SoC despite poor initial values, enabling improved performance for mobile robots.

This paper presents the design of a research platform for autonomous driving applications, the Delft's Autonomous-driving Robotic Testbed (DART). Our goal was to design a small-scale car-like robot equipped with all the hardware needed for on-board navigation and control while keeping it cost-effective and easy to replicate. To develop DART, we built on an existing off-the-shelf model and augmented its sensor suite to improve its capabilities for control and motion planning tasks. We detail the hardware setup and the system identification challenges to derive the vehicle's models. Furthermore, we present some use cases where we used DART to test different motion planning applications to show the versatility of the platform. Finally, we provide a git repository with all the details to replicate DART, complete with a simulation environment and the data used for system identification.

We present a novel Curvature-Aware Model Pre-dictive Contouring Control (CA-MPCC) formulation for mobile robotics motion planning. Our method aims at generalizing the traditional contouring control formulation derived from machining to autonomous driving applications. The proposed controller is able of handling sharp curvatures in the reference path while subject to non-linear constraints, such as lane boundaries and dynamic obstacle collision avoidance. Com-pared to a standard MPCC formulation, our method improves the reliability of the path-following algorithm and simplifies the tuning, while preserving real-time capabilities. We validate our findings in both simulations and experiments on a scaled-down car-like robot.

This letter proposes a distributed strategy to achieve both persistent monitoring and target detection in a rectangular and obstacle-free environment. Each robot has to repeatedly follow a smooth trajectory and avoid collisions with other robots. To achieve this goal, we rely on the time-inverted Kuramoto dynamics and the use of Lissajous curves. We analyze the resiliency of the system to perturbations or temporary failures, and we validate our approach through both simulations and experiments on real robotic platforms. In the letter, we adopt Model Predictive Contouring Control as a low level controller to minimize the tracking error while accounting for the robots' dynamical constraints and the control inputs saturation. The results obtained in the experiments are in accordance with the simulations.

This work presents a method for multi-robot coordination based on a novel distributed nonlinear model predictive control (NMPC) formulation for trajectory optimization and its modified version to mitigate the effects of packet losses and delays in the communication among the robots. Our algorithms consider that each robot is equipped with an onboard computation unit to solve a local control problem and communicate with neighboring autonomous robots via a wireless network. The difference between the two proposed methods is in the way the robots exchange information to coordinate. The information exchange can occur in a following: 1) synchronous or 2) asynchronous fashion. By relying on the theory of the nonconvex alternating direction method of multipliers (ADMM), we show that the proposed solutions converge to a (local) solution of the centralized problem. For both algorithms, the communication exchange preserves the safety of the robots; that is, collisions with neighboring autonomous robots are prevented. The proposed approaches can be applied to various multi-robot scenarios and robot models. In this work, we assess our methods, both in simulation and with experiments, for the coordination of a team of autonomous vehicles in the following: 1) an unsupervised intersection crossing and 2) the platooning scenarios.