Robot Operating System (ROS) is very popular in robotic software development. To ease the process management of ROS programs, ROS provides a speciallifecyclemechanism that can conveniently manage the state of each running process, which often involves resource allocation, initialization, and release; and this mechanism has been widely used in real-world ROS programs. However, due to code concurrency of ROS programs, a lifecycle-related function is inevitably concurrently executed with other functions, introducing the security risk of dangerous concurrency bugs involving null-pointer dereference and use after free. Due to the non-determinism of thread scheduling, these concurrency bugs are difficult to find and reproduce. In this paper, we design and implement a new coverage-guided fuzzing framework named ROCF, which can effectively detect and reproduce lifecycle-related concurrency bugs in ROS programs, with two novel techniques. First, we propose alifecycle-aware fuzzing approachthat useslifecycle pair sequenceas a new coverage metric to effectively describe lifecycle-related thread interleavings, for input-mutation guidance of ROS concurrency fuzzing. Second, we propose aheuristic-based reproducing methodthat identifies minimal input sequences that can stably and efficiently reproduce the found concurrency bugs, with strategical input pruning and delay injection. We evaluate ROCF on eight popular robotic programs in ROS2, and it finds 32 new and real concurrency bugs, all of which have been confirmed by ROS developers, and 19 have been assigned CVE IDs.

Semantic segmentation is pivotal in autonomous train perception, significantly impacting the system’s intelligence and reliability. However, its performance in railway scenes is hindered by various challenges, including severe weather conditions, low-light situations, tunnel settings, and diverse and dynamic unstructured scenes. To address these challenges, this study proposes WaveCRNet, a novel architecture for real-time semantic segmentation in challenging conditions, simulating wavelet-constrained PID controller in feature and wavelet space. This study first designs an effective wavelet information enhancement algorithm using a differentiable wavelet transform to bridge the gap between the wavelet and feature information domains. Then, the wavelet-guided attention pag module (WAPM) is introduced to guide the learning and fusion of detailed features based on wavelet priors. Moreover, the traditional wavelet transforms are spectral aliasing and shift sensitivity. Inspired by dual-tree complex wavelet transform (DTCWT), the DTCWT-based channel reconstruction module (DCRM) is designed to assist the channel-based learning of boundary information from coarse to fine. Finally, the proposed architecture is evaluated by the public dataset RailSem19. The experimental results validate the consistent performance gains between accuracy and inference speed, improving by 63.7% mIoU and 87 FPS, surpassing those of the advanced methods for real-time segmentation.

This research explores the inherent vulnerability of nonlinear vehicle platoons characterized by the oscillatory behavior triggered by external perturbations. The perturbation exerted on the vehicle platoon is regarded as an external force on an object. Following the mechanical vibration analysis in mechanics, this research proposes a vibration-theoretic approach that advances our understanding of platoon vulnerability from two aspects. First, the proposed approach introduces damping intensity to characterize vehicular platoon vulnerability, which divides platoon oscillations into two types, i.e., underdamped and overdamped. The damping intensity measures the platoon’s recovery strength in responding to perturbations. Second, the proposed approach can obtain the resonance frequency of a nonlinear vehicle platoon, where resonance amplifies platoon oscillation magnitude when the external perturbation frequency equals the platoon’s damping oscillation frequency. The main contribution of this research lies in the analytical derivation of the closed-form formulas of damping intensity and resonance frequency. In particular, the proposed approach formulates platoon dynamics under perturbation as a second-order non-homogeneous ordinary differential equation, enabling rigorous derivations and analyses for platoons with complicated nonlinear car-following behaviors. Through simulations built on real-world data, this paper demonstrates that an overdamped vehicle platoon is more robust against perturbations, and an underdamped platoon can be destabilized easily by exerting a perturbation at the platoon’s resonance frequency. The theoretical derivations and simulation results shed light on the design of reliable platooning control, either for human-driven or automated vehicles, to suppress the adverse effects of oscillations.

Accurate train positioning is crucial for train safety. In this paper, we propose a train positioning method which fuses vison and millimeter-wave radar data. The proposed method contains two parts: loop closure detection (LCD) and radar-based odometry. The loop closure detection part fuses the convolutional neural network (CNN) features and the line features to achieve accurate key location detection. The radar-based odometry part proposes a train speed measurement algorithm using millimeter-wave radar, and combines the results of loop closure detection to further realize train positioning. Experiments conducted on the Hong Kong metro Tsuen Wan line show that our proposed loop closure detection can achieve an efficient key location detection with 98.57% precision and 99.37% recall; the speed detection method fulfills the ETCS requirements; and the relative error of the proposed train positioning method is 0.45%. Besides, the proposed method has been applied on the Hong Kong Metro TSUEN WAN line.

Lane detection plays a vital part in autonomous driving. Conventional studies rely on less robust hand-craft features, while deep learning has improved the performance of lane detection to a great extent. Different from dominant methods based on semantic segmentation, this paper proposes an end-to-end framework named DevNet, which combines deviation awareness with semantic features based on point estimation. It consists of two modules to capture more representative features by integrating information of distance deviation and angle which helps to tackle diverse driving conditions in real environments, such as dim or shiny light conditions, crowdedness, and vanishing lanes. Experiments on public datasets indicate that the proposed method achieves favorable performance when compared with the state-of-the-art methods.

The sparse array design for adaptive beamforming has been recently formulated into combinatorial antenna selection problems, which belong to notorious NP-hard problems. As the commonly deployed convex relaxation algorithms are susceptible to local optima, several trials with different initial points are conducted for the global optima. Moreover, the high computational load of optimization techniques prohibits the real-time adaptive array reconfiguration. In this work, we propose to utilize machine learning algorithms, specifically support vector machine (SVM) and artificial neural network (ANN), for solving combinatorial antenna selection problems. Numerical examples are presented to validate the effectiveness and efficiency of machine learning algorithms for sparse array design. Moreover, the SVM based antenna selection is robust against DOA estimate uncertainties.