This paper proposes a new strategy for collision avoidance system leveraging Time-to-Collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating a deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to traditional TTC -based approaches.

This work addresses the problem of designing a visual servo controller for a multirotor vehicle, with the end goal of tracking a moving spherical target with unknown radius. To address this problem, we first transform two bearing measurements provided by a camera sensor into a bearing-angle pair. We then use this information to derive the system's dynamics in a new set of coordinates, where the angle measurement is used to quantify a relative distance to the target. Building on this system representation, we design an adaptive nonlinear control algorithm that takes advantage of the properties of the new system geometry and assumes that the target follows a constant acceleration model. Simulation results illustrate the performance of the proposed control algorithm.

Control Barrier Functions (CBFs) are a powerful tool for ensuring the safety of autonomous systems, yet applying them to nonholonomic robots in cluttered, dynamic environments remains an open challenge. State-of-the-art methods often rely on collision-cone or velocity-obstacle constraints which, by only considering the angle of the relative velocity, are inherently conservative and can render the CBF-based quadratic program infeasible, particularly in dense scenarios. To address this issue, we propose a Dynamic Parabolic Control Barrier Function (DPCBF) that defines the safe set using a parabolic boundary. The parabola's vertex and curvature dynamically adapt based on both the distance to an obstacle and the magnitude of the relative velocity, creating a less restrictive safety constraint. We prove that the proposed DPCBF is valid for a kinematic bicycle model subject to input constraints. Extensive comparative simulations demonstrate that our DPCBF-based controller significantly enhances navigation success rates and QP feasibility compared to baseline methods. Our approach successfully navigates through dense environments with up to 100 dynamic obstacles, scenarios where collision cone-based methods fail due to infeasibility.

-- Autonomous vehicles require reliable hazard detection. However, primary sensor systems may miss near-field obstacles, resulting in safety risks. Although a dedicated fast-reacting near-field monitoring system can mitigate this, it typically suffers from false positives. T o mitigate these, in this paper, we introduce three monitoring strategies based on dynamic spatial properties, relevant object sizes, and motion-aware prediction. In experiments in a validated simulation, we compare the initial monitoring strategy against the proposed improvements. The results demonstrate that the proposed strategies can significantly improve the reliability of near-field monitoring systems. Advanced Driver Assistance Systems (ADASs) are rapidly advancing toward improved automation and safety in transportation; however, ensuring robust and reliable safety of self-driving vehicles remains a critical challenge. While Autonomous Emergency Braking Systems (AEBSs) have demonstrated potential in reducing collision risks, these systems often rely on the same sensor setup as the high-level driving system, which can miss hazards outside the sensors' Field-of-View (FOV). Especially in complex urban environments, such blind spots underscore the need for dedicated near-field monitoring systems as an additional safety layer.

Fixed-wing UAVs have transformed the transportation system with their high flight speed and long endurance, yet their safe operation in increasingly cluttered environments depends heavily on effective collision avoidance techniques. This paper presents a novel method for safely navigating an aircraft along a desired route while avoiding moving obstacles. We utilize a class of control barrier functions (CBFs) based on collision cones to ensure the relative velocity between the aircraft and the obstacle consistently avoids a cone of vectors that might lead to a collision. By demonstrating that the proposed constraint is a valid CBF for the aircraft, we can leverage its real-time implementation via Quadratic Programs (QPs), termed the CBF-QPs. Validation includes simulating control law along trajectories, showing effectiveness in both static and moving obstacle scenarios.

In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier; (ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles; and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles.

This paper proposes and develops a physics-inspired neural network (PiNN) for learning the parameters of commercially implemented adaptive cruise control (ACC) systems in automotive industry. To emulate the core functionality of stock ACC systems, which have proprietary control logic and undisclosed parameters, the constant time-headway policy (CTHP) is adopted. Leveraging the multi-layer artificial neural networks as universal approximators, the developed PiNN serves as a surrogate model for the longitudinal dynamics of ACC-engaged vehicles, efficiently learning the unknown parameters of the CTHP. The ability of the PiNN to infer the unknown ACC parameters is meticulous evaluated using both synthetic and high-fidelity empirical data of space-gap and relative velocity involving ACC-engaged vehicles in platoon formation. The results have demonstrated the superior predictive ability of the proposed PiNN in learning the unknown design parameters of stock ACC systems from different car manufacturers. The set of ACC model parameters obtained from the PiNN revealed that the stock ACC systems of the considered vehicles in three experimental campaigns are neither $L_2$ nor $L_\infty$ string stable.

In recent years, computer simulators of rigid-body systems have been successfully used to improve and expand the field of developing new space robots, becoming a leading tool for the preliminary investigation and evaluation of space robotic missions. However, the impressive progress in performance has not been matched yet by an improvement in modelling capabilities, which remain limited to very basic representations of real systems. We present a new approach to modelling and simulation of collision-inclusive multibody dynamics by leveraging symbolic models generated by a computer algebra system (CAS). While similar investigations into contact dynamics on other domains exploit pre-existing models of common multibody systems (e.g., industrial robot arms, humanoids, and wheeled robots), our focus is on allowing researchers to develop models of novel designs of systems that are not as common or yet to be fabricated: e.g., small spacecraft manipulators. In this paper, we demonstrate the usefulness of our approach to investigate spacecraft-debris collision dynamics.

In this paper, the collision avoidance problem for non-holonomic robots moving at constant linear speeds in the 2-D plane is considered. The maneuvers to avoid collisions are designed using dynamic vortex potential fields (PFs) and their negative gradients; this formulation leads to a reciprocal behaviour between the robots, denoted as being cooperative. The repulsive field is selected as a function of the velocity and position of a robot relative to another and introducing vorticity in its definition guarantees the absence of local minima. Such a repulsive field is activated by a robot only when it is on a collision path with other mobile robots or stationary obstacles. By analysing the kinematics-based engagement dynamics in polar coordinates, it is shown that a cooperative robot is able to avoid collisions with non-cooperating robots, such as stationary and constant velocity robots, as well as those actively seeking to collide with it. Conditions on the PF parameters are identified that ensure collision avoidance for all cases. Experimental results acquired using a mobile robot platform support the theoretical contributions.

Abstract--Interstellar objects (ISOs) are fascinating and underexplored be best implemented using small spacecraft. The unification of celestial objects, providing physical laboratories to ISO detection, orbit characterization, and cruise trajectory with understand the formation of our solar system and probe the learning-based G&C algorithms for accurate low-V flybys composition and properties of material formed in exoplanetary represents a nearly end-to-end simulation and assessment of a systems. The recent Planetary Science and Astrobiology mission to visit an ISO. This process is simulated using JPL's Decadal Survey emphasized that a dedicated mission to an interstellar SmallSat Development Testbed, which determines the feasibility object would have high scientific value. A dedicated ISOs with varying characteristics, including a discussion of state spacecraft could resolve the shape, rotation properties, surface covariance estimation over the course of a cruise, handoffs from morphology, and composition of an asteroid-like ISO. Mass traditional navigation approaches to novel autonomous navigation spectroscopy techniques can probe the gas composition of a for fast flyby regimes, and overall recommendations about comet-like ISO.