Abstract Cyber-physical systems (CPS) with reinforcement learning (RL)-based controllers are increasingly being deployed in complex physical environments such as autonomous vehicles, the Internet-of-Things (IoT), and smart cities. An important property of a CPS is tolerance; i.e., its ability to function safely under possible disturbances and uncertainties in the actual operation. In this paper, we introduce a new, expressive notion of tolerance that describes how well a controller is capable of satisfying a desired system requirement, specified using Signal Temporal Logic (STL), under possible deviations in the system. Based on this definition, we propose a novel analysis problem, called the tolerance falsification problem, which involves finding small deviations that result in a violation of the given requirement. We present a novel, two-layer simulation-based analysis framework and a novel search heuristic for finding small tolerance violations. To evaluate our approach, we construct a set of benchmark problems where system parameters can be configured to represent different types of uncertainties and disturbances in the system. Our evaluation shows that our falsification approach and heuristic can effectively find small tolerance violations.

The exploration of software Over-the-Air (OTA) updates for automotive applications is currently very limited. Our work introduces an edge-assisted framework for automotive OTA updates that carefully accounts for various factors, including different software models in vehicles, communication distances, and cluster sizes. We present valuable insights using key evaluation metrics like update speed, data transmission efficiency, and success rate, accompanied by a thorough scalability analysis. Our research involves three distinct vehicle software models: ResNet-18 (46.8 MB), ResNet-50 (102.5 MB), and Faster R-CNN (175.2 MB). These models are used to evaluate update performance across eight distance categories ranging from 0 to 21 meters with a 3-meter interval. We also utilize diverse computing platforms to assess the success rate and conduct a comprehensive scalability analysis. This innovative approach significantly advances our understanding and practical implementation of OTA updates in the automotive field.

Vehicular cooperative perception aims to provide connected and automated vehicles (CAVs) with a longer and wider sensing range, making perception less susceptible to occlusions. However, this prospect is dimmed by the imperfection of onboard localization sensors such as Global Navigation Satellite Systems (GNSS), which can cause errors in aligning over-the-air perception data (from a remote vehicle) with a Host vehicle’s (HV’s) local observation. To mitigate this challenge, we propose a novel LiDAR-based relative localization framework based on the iterative closest point (ICP) algorithm. The framework seeks to estimate the correct transformation matrix between a pair of CAVs’ coordinate systems, through exchanging and matching a limited yet carefully chosen set of point clouds and usage of a coarse 2D map. From the deployment perspective, this means our framework only consumes conservative bandwidth in data transmission and can run efficiently with limited resources. Extensive evaluations on both synthetic dataset (COMAP) and KITTI-360 show that our proposed framework achieves state-of-the-art (SOTA) performance in cooperative localization. Therefore, it can be integrated with any upper-stream data fusion algorithm and serves as a preprocessor for high-quality cooperative perception.

A Digital Twin is a digital replica of a living or nonliving physical entity, and this emerging technology attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, a mobility digital twin (MDT) framework is developed, which is defined as an artificial intelligence (AI)-based data-driven cloud–edge–device framework for mobility services. This MDT consists of three building blocks in the physical space (namely,Human,Vehicle, andTraffic), and their associated Digital Twins in the digital space. An example cloud–edge architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to fulfill its digital functionalities of storage, modeling, learning, simulation, and prediction. A case study of the personalized adaptive cruise control (P-ACC) system is conducted, which integrates the key microservices of all three digital building blocks of the MDT framework: 1) theHuman Digital Twinwith user management and driver type classification; 2) theVehicle Digital Twinwith cloud-based advanced driver-assistance systems (ADAS); and 3) theTraffic Digital Twinwith traffic flow monitoring and variable speed limit. Future challenges of the proposed MDT framework are discussed toward the end of the article, including standardization, AI for computing, public or private cloud service, and network heterogeneity.