Vulnerable road users (VRUs) face high collision risks in mixed traffic, yet most existing safety systems prioritize driver or vehicle assistance over direct VRU support. This paper presents ARCAS, a real-time augmented reality (AR) collision avoidance system that provides personalized spatial alerts to VRUs via wearable AR headsets. By fusing roadside 360° 3D LiDAR with SLAM-based headset tracking and an automatic 3D calibration procedure, ARCAS accurately overlays world-locked 3D bounding boxes and directional arrows onto approaching hazards in the user's passthrough view. The system also enables multi-headset coordination through shared world anchoring. Evaluated in real-world pedestrian interactions with e-scooters and vehicles (180 trials), ARCAS nearly doubles pedestrians' time to collision and increases counterparts' reaction margins by up to 4x compared to unaided eye conditions. Results validate the feasibility and effectiveness of LiDAR-driven AR guidance and highlight the potential of wearable AR as a promising next generation safety tool for urban mobility.

Recently, geospatial artificial intelligence (GeoAI) has risen as a set of essential technologies for urban mobility pattern mining and understanding. However, traditional deep learning models are constrained by their high data dependency and limited interpretability. This study introduces the Knowledge-Driven Semantic Tree (KD-ST) model, a novel GeoAI framework that integrates structured semantic descriptions with graph-based learning to enhance geospatial modeling on e-scooter ridership classification. By incorporating a street knowledge structure into the GeoAI model architecture, KD-ST bridges the gap between purely data-driven methods and knowledge-informed urban analytics, improving classification performance and model transparency. We conducted case studies in four major U.S. cities, including Austin, Phoenix, Denver, and Washington, D.C., to evaluate the proposed KD-ST model’s performance. The proposed model outperformed baseline models by 12.1% to 156.5% as for the F1 score. Moreover, to enhance transparency and reliability, key internal parameters were extracted to visualize and analyze the learned hierarchical knowledge structure. Results indicate that domain knowledge provides useful information for the design of deep learning models and improves model performance. Furthermore, the model achieved higher transferability among cities with more similar urban contexts, which provides valuable insights for e-scooter planners on model choice.

In Internet of Things (IoT) scenarios, the limited computing resources and energy constraints of devices, alongside the growing demand for real-time applications, make secure image transmission challenging. To address this issue, we propose a lightweight image encryption scheme based on chaotic map. Firstly, a new 3-D inverse proportional chaotic map (3D-IPCM) is designed with good robustness. Dynamics confirms that it possesses key characteristics, including a broad and continuous chaotic range, all positive Lyapunov exponents (LEs), high permutation entropy (PE) complexity and even distribution. Then, a new pseudorandom number generator (PRNG) is designed based on this map, which successfully passes all NIST and TestU01 tests, even with 16-bit calculation precision. Next, this PRNG is employed for image encryption in IoT scenarios. In the cryptosystem, a chromosome crossover (CC)-based scrambling algorithm is proposed, along with a diffusion algorithm that achieves strong resistance to differential attack in just two rounds of row diffusion. Simulation and analysis verify that the cryptosystem has strong resistance to common attacks and low cost. For$256 \times 256$images, its average number of pixels change rate (NPCR) and unified average changing intensity (UACI) are 100% and 33.40%, respectively, and the encryption time is only 7.4 ms. Ultimately, we implement the algorithm on FPGA, thus confirming its capacity for parallel acceleration.

Rising demand for last-mile deliveries in the logistics sector has prompted the adoption of Autonomous Delivery Robots (ADRs) and electric trucks (eTrucks) for their efficiency and cost-effectiveness. This paper proposes an optimization model for an integrated eTruck-and-ADR system. The model employs a range of information sources to optimize vehicle routing and robot allocation, emphasizing energy efficiency and operating cost. This includes incorporating Geographic Information System (GIS) to estimate customer demand based on demographics and utilizing a battery aging/degradation model to account for hardware depreciation. A metaheuristic Genetic Algorithm is employed to solve optimal vehicle routing and customer node clustering. In a simulated case study conducted with real GIS and geographic data, the proposed model demonstrates efficacy in determining the optimal number of ADRs for specific census tracts, with a cost breakdown highlighting the dominance of human labor costs.

At present, design of discrete memristor based chaotic maps starts to attract the attention of the scientists, but it is still in its incipient stage. In this paper, spatiotemporal chaos in the Sine map lattice with discrete memristor coupling is investigated. Firstly, the$3\times m$higher dimensional chaotic map is proposed, where there are$m$discrete memristors and$m$state variable difference items as the inputs of the discrete memristors. Since it is a spatiotemporal chaotic system, thus it can generate massive chaotic sequences according to the system dimension. Secondly, dynamical characteristics of the system is carried out theoretically and numerically. It shows that there are$m$positive Lyapunov exponents with high complexity. The two examples with one memristor and two memristors are analyzed, and it indicates that the system has rich dynamics including hyperchaos and multistability. Finally, analogue circuit and DSP digital circuit of the two illustrative examples and an Knowm memristor based example are designed to verify the physical realizability of the proposed discrete memristor chaotic maps.

A universal modular plus parallel (MPP) method is proposed to construct enhanced chaotic maps, including one-dimensional MPP chaotic map (1D-MPPCM) and high-dimensional MPPCM (HD-MPPCM). It is theoretically proved that 1D-MPPCM model can significantly increase the Lyapunov exponent (LE) and parameter range of seed chaotic maps. To further increase the system dimension, the HD-MPPCM model is established through the close-loop parallel coupling mechanism. Based on several typical seed chaotic maps, some new parallel chaotic maps are obtained by self-parallel and hybrid-parallel, and their dynamics are analyzed by phase diagram, LEs, permutation entropy (PE) complexity and statistic$\chi ^{2}$. The simulation results show that the proposed maps have large maximum Lyapunov exponent (MLE), PE complexity, and uniform distribution. In particular, HD-MPPCMs have some interesting characteristics, such as full positive LEs, hyperchaotic behavior, global chaos, and full attractor distribution, which are the potential model for engineering applications. To further verify the practicability, the proposed maps are implemented on DSP platform, and applied to pseudo-random number generator (PRNG).