Prototyping and validating hardware–software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt to develop such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single- and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path-planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things.

Attitude estimation is critical for navigation and control systems, and low-cost IMUs have become a preferred choice for many applications due to their affordability, compact size, light weight, and long lifespan. However, traditional attitude estimation algorithms, such as complementary filtering (CF) and quaternion-based extended Kalman filter (EKF), often perform poorly when the carrier is subject to non-gravitational accelerations or external disturbances. This paper presents a measurement adaptive reduced direction cosine matrix (MA-RDCM) attitude estimation algorithm. The algorithm constructs an EKF using a reduced direction cosine matrix as the state vector, integrating measurement data from MEMS gyroscopes and accelerometers for real-time attitude estimation. Furthermore, a measurement adaptive adjustment strategy is proposed, which dynamically corrects the measurement noise covariance matrix in real-time to address the degradation in attitude estimation accuracy caused by non-gravitational accelerations during carrier motion. The adaptive adjustment strategy effectively reduces the impact of non-gravitational accelerations and external disturbances, improving the accuracy and robustness of attitude estimation for low-cost IMU. The performance of the algorithm is evaluated through computer simulations and land vehicle experiments. The results show that the MA-RDCM algorithm significantly enhances attitude accuracy, particularly in dynamic scenarios, improving performance by approximately 33% compared to traditional methods, showcasing its significant advantages.

This paper explores the use of few-shot learning in Wi-Fi-based indoor positioning, utilizing convolutional neural networks (CNNs) combined with meta-learning techniques to enhance the accuracy and efficiency of positioning systems. The focus is on addressing the challenge of limited labeled data, a prevalent issue in extensive indoor environments. The study explores various scenarios, comparing the performance of the base CNN and meta-learning models. The meta-learning approach involves few-shot learning tasks, such as three-way N-shot, five-way N-shot, etc., to enhance the model’s ability to generalize from limited data. The experiments were conducted across various scenarios, evaluating the performance of the models with different numbers of samples per class (K) after filtering by cosine similarity (FCS) during both the stages of data preprocessing and meta-learning. The scenarios included both base classes and novel classes, with and without meta-learning. The results indicated that the base CNN model achieved varying accuracy levels depending on the scenario and the number of samples per class retained after FCS. Meta-learning performed acceptably in scenarios with fewer samples, which are the distinct datasets pertaining to novel classes. With 20 samples per class, the base CNN achieved an accuracy of 0.80 during the pre-training stage, while meta-learning (three-way one-shot) achieved an accuracy of 0.78 on a new small dataset with novel classes.

This study compares the effectiveness of three methods—Fréchet Distance, Dynamic Time Warping (DTW), and Convolutional Neural Networks (CNNs)—in detecting similarities and pattern recognition in time series. It proposes a Pattern-Driven Case Generator (PDCG) framework to automate the creation of labeled time series data for training CNN models, addressing the challenge of manual dataset curation. By injecting controlled noise and interpolating diverse shapes (e.g., W/M/nAn/vVv), a PDCG synthesizes realistic training data that enhances model robustness. Experimental results demonstrate that the CNN model, trained with 10,000 PDCG-generated cases, achieves 86–98% accuracy in pattern recognition, outperforming traditional methods (Fréchet and DTW) for complex, misaligned, and variable-length sequences. The PDCG-enhanced CNN’s scalability and adaptability improve with larger datasets, validating the PDCG’s efficacy in bridging simulation and real-world applications.