As the complexity of autonomous vehicles (AVs) continues to increase and artificial intelligence algorithms are becoming increasingly ubiquitous, a novel safety concern known as the safety of the intended functionality (SOTIF) has emerged, presenting significant challenges to the widespread deployment of AVs. SOTIF focuses on issues arising from the functional insufficiencies of the AVs’ intended functionality or its implementation, apart from conventional safety considerations. From the systems engineering standpoint, this study offers a comprehensive exploration of the SOTIF landscape by reviewing academic research, practical activities, challenges, and perspectives across the development, verification, validation, and operation phases. Academic research encompasses system-level SOTIF studies and algorithm-related SOTIF issues and solutions. Moreover, it encapsulates practical SOTIF activities undertaken by corporations, government entities, and academic institutions spanning international and Chinese contexts, focusing on the overarching methodologies and practices in different phases. Finally, the paper presents future challenges and outlook pertaining to the development, verification, validation, and operation phases, motivating stakeholders to address the remaining obstacles and challenges.
This study investigates resilient platoon control for constrained intelligent and connected vehicles (ICVs) against F-local Byzantine attacks. We introduce a resilient distributed model-predictive platooning control framework for such ICVs. This framework seamlessly integrates the predesigned optimal control with distributed model predictive control (DMPC) optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles. Notably, our strategy uses previously broadcasted information and a specialized convex set, termed the “resilience set”, to identify unreliable data. This approach significantly eases graph robustness prerequisites, requiring only an (F + 1)-robust graph, in contrast to the established mean sequence reduced algorithms, which require a minimum (2F + 1)-robust graph. Additionally, we introduce a verification algorithm to restore trust in vehicles under minor attacks, further reducing communication network robustness. Our analysis demonstrates the recursive feasibility of the DMPC optimization. Furthermore, the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs, while ensuring constraint compliance and cybersecurity. Simulation results verify the effectiveness of our theoretical findings.
The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers. Using formal or digital methods, natural language traffic rules can be translated into machine language and used by autonomous vehicles. In this paper, a translation flow is designed. Beyond the translation, a deeper examination is required, because the semantics of natural languages are rich and complex, and frequently contain hidden assumptions. The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved. In response, we propose a method of formal verification that combines equivalence verification with model checking. Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method. In addition, we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations. The experimental findings indicate that our digital rules utilizing metric temporal logic (MTL) can be easily incorporated into simulation platforms and autonomous driving systems (ADS).
This study presents a general optimal trajectory planning (GOTP) framework for autonomous vehicles (AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently. Firstly, we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline. Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve. Considering the road constraints and vehicle dynamics, limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system. Furthermore, in selecting the optimal trajectory, we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’ behavior and summarizing their manipulation characteristics of “seeking benefits and avoiding losses.” Finally, by integrating the idea of receding-horizon optimization, the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility, optimality, and adaptability. Extensive simulations and experiments are performed, and the results demonstrate the framework’s feasibility and effectiveness, which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants. Moreover, we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’ manipulation.
While autonomous vehicles are vital components of intelligent transportation systems, ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving. Therefore, we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles. The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety. Specifically, an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics. In addition, an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics. Moreover, we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model. Finally, the proposed approach is evaluated through both simulations and experiments. These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.
Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles. Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions. However, they suffer from over-conservatism, potentially resulting in false-positive risk events in complicated real-world applications. In this paper, we combine two reachability analysis techniques, a backward reachable set (BRS) and a stochastic forward reachable set (FRS), and propose an integrated probabilistic collision-detection framework for highway driving. Within this framework, we can first use a BRS to formally check whether a two-vehicle interaction is safe; otherwise, a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step. Thus, the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data. The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising for real-world applications.
Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment. This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data- and model-driven method. First, a data-driven decision-making module based on deep reinforcement learning (DRL) is developed to pursue a rational driving performance as much as possible. Then, model predictive control (MPC) is employed to execute both longitudinal and lateral motion planning tasks. Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements. Finally, two principles of safety and rationality for the self-evolution of autonomous driving are proposed. A motion envelope is established and embedded into a rational exploration and exploitation scheme, which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent. Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted, and the results show that the proposed online-evolution framework is able to generate safer, more rational, and more efficient driving action in a real-world environment.
Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities. For instance, human occupants are passengers and do not have direct vehicle control in fully automated cars (i.e., driverless cars). An interesting question is whether users are responsible for the accidents of these cars. Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control. Here, we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments (N = 2668). We compared the responsibility attributed to the occupants in three conditions: an owner in his private fully automated car, a passenger in a driverless robotaxi, and a passenger in a conventional taxi, where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury. In contrast to normative analyses, we show that the occupants of driverless cars (private cars and robotaxis) are attributed more responsibility than conventional taxi passengers. This dilemma is robust across different contexts (e.g., participants from China vs the Republic of Korea, participants with first- vs third-person perspectives, and occupant presence vs absence). Furthermore, we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars. Our findings suggest that when driverless vehicles (private cars and taxis) cause harm, their users may face more social pressure, which public discourse and legal regulations should manage appropriately.
The forward design of trajectory planning strategies requires preset trajectory optimization functions, resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits. In addition, owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios, it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters. Therefore, an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed. First, numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset. Subsequently, a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory. Furthermore, a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function, and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed. Finally, the proposed strategy is verified based on real driving scenarios. The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the “emergency degree” of obstacle avoidance and the state of the vehicle. Moreover, this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories, effectively improving the adaptability and acceptability of trajectories in driving scenarios.
Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles (AVs). The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer. To address this issue, this study proposes a safe motion planning and control (SMPAC) framework for AVs. For the control layer, a dynamic model including multi-dimensional uncertainties is established. A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set. A flexible tube with varying cross-sections is constructed to reduce the controller conservatism. For the planning layer, a concept of safety sets, representing the geometric boundaries of the ego vehicle and obstacles under uncertainties, is proposed. The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories. An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles. A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC. The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.
Active suspension systems (ASSs) have been proposed and developed for a few decades, and have now once again become a thriving topic in both academia and industry, due to the high demand for driving comfort and safety and the compatibility of ASSs with vehicle electrification and autonomy. Existing review papers on ASSs mainly cover dynamics modeling and robust control; however, the gap between academic research outcomes and industrial application requirements has not yet been bridged, hindering most ASS research knowledge from being transferred to vehicle companies. This paper comprehensively reviews advances in ASSs for road vehicles, with a focus on hardware structures and control strategies. In particular, state-of-the-art ASSs that have been recently adopted in production cars are discussed in detail, including the representative solutions of Mercedes active body control (ABC) and Audi predictive active suspension; novel concepts that could become alternative candidates are also introduced, including series active variable geometry suspension, and the active wheel-alignment system. ASSs with compact structure, small mass increment, low power consumption, high-frequency response, acceptable economic costs, and high reliability are more likely to be adopted by car manufacturers. In terms of control strategies, the development of future ASSs aims not only to stabilize the chassis attitude and attenuate the chassis vibration, but also to enable ASSs to cooperate with other modules (e.g., steering and braking) and sensors (e.g., cameras) within a car, and even with high-level decision-making (e.g., reference driving speed) in the overall transportation system—strategies that will be compatible with the rapidly developing electric and autonomous vehicles.
High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles (IoVs). However, it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment. In order to protect data privacy and improve data learning efficiency in knowledge sharing, we propose an asynchronous federated broad learning (FBL) framework that integrates broad learning (BL) into federated learning (FL). In FBL, we design a broad fully connected model (BFCM) as a local model for training client data. To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients, we construct a joint resource allocation and reconfigurable intelligent surface (RIS) configuration optimization framework for FBL. The problem is decoupled into two convex subproblems. Aiming to improve the resource scheduling efficiency in FBL, a double Davidon-Fletcher-Powell (DDFP) algorithm is presented to solve the time slot allocation and RIS configuration problem. Based on the results of resource scheduling, we design a reward-allocation algorithm based on federated incentive learning (FIL) in FBL to compensate clients for their costs. The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency, accuracy, and cost for knowledge sharing in the IoV.
Ultrafine-grained pure metals and their alloys have high strength and low ductility. In this study, cryorolling under different strains followed by low-temperature short-time annealing was used to fabricate pure nickel sheets combining high strength with good ductility. The results show that, for different cryorolling strains, the uniform elongation was greatly increased without sacrificing the strength after annealing. A yield strength of 607 MPa and a uniform elongation of 11.7% were obtained after annealing at a small cryorolling strain (ε = 0.22), while annealing at a large cryorolling strain (ε = 1.6) resulted in a yield strength of 990 MPa and a uniform elongation of 6.4%. X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and electron backscattered diffraction (EBSD) were used to characterize the microstructure of the specimens and showed that the high strength could be attributed to strain hardening during cryorolling, with an additional contribution from grain refinement and the formation of dislocation walls. The high ductility could be attributed to annealing twins and micro-shear bands during stretching, which improved the strain hardening capacity. The results show that the synergistic effect of strength and ductility can be regulated through low-temperature short-time annealing with different cryorolling strains, which provides a new reference for the design of future thermo-mechanical processes.
Blue energy, which includes rainfall, tidal current, wave, and water-flow energy, is a promising renewable resource, although its exploitation is limited by current technologies and thus remains low. This form of energy is mainly harvested by electromagnetic generators (EMGs), which generate electricity via Lorenz force-driven electron flows. Triboelectric nanogenerators (TENGs) and TENG networks exhibit superiority over EMGs in low-frequency and high-entropy energy harvesting as a new approach for blue energy harvesting. A TENG produces electrical outputs by adopting the mechanism of Maxwell’s displacement current. To date, a series of research efforts have been made to optimize the structure and performance of TENGs for effective blue energy harvesting and marine environmental applications. Despite the great progress that has been achieved in the use of TENGs in this context so far, continuous exploration is required in energy conversion, device durability, power management, and environmental applications. This review reports on advances in TENGs for blue energy harvesting and marine environmental monitoring. It introduces the theoretical foundations of TENGs and discusses advanced TENG prototypes for blue energy harvesting, including TENG structures that function in freestanding and contact-separation modes. Performance enhancement strategies for TENGs intended for blue energy harvesting are also summarized. Finally, marine environmental applications of TENGs based on blue energy harvesting are discussed.
Identifying workers’ construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress. However, current activity analysis methods for construction workers rely solely on manual observations and recordings, which consumes considerable time and has high labor costs. Researchers have focused on monitoring on-site construction activities of workers. However, when multiple workers are working together, current research cannot accurately and automatically identify the construction activity. This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers. In this framework, multiple deep neural network models are designed and used to complete worker key point extraction, worker tracking, and worker construction activity analysis. The designed framework was tested at an actual construction site, and activity recognition for multiple workers was performed, indicating the feasibility of the framework for the automated monitoring of work efficiency.
This study investigates the long-term performance of laboratory dam concrete in different curing environments over ten years and the microstructure of 17-year-old laboratory concrete and actual concrete cores drilled from the Three Gorges Dam. The mechanical properties of the laboratory dam concrete, whether cured in natural or standard environments, continued to improve over time. Furthermore, the laboratory dam concrete exhibited good resistance to diffusion and a refined microstructure after 17 years. However, curing and long-term exposure to the local natural environment reduced the frost resistance. Microstructural analyses of the laboratory concrete samples demonstrated that moderate-heat cement and fine fly ash (FA) particles were almost fully hydrated to form compact microstructures consisting of large quantities of homogeneous calcium (alumino)silicate hydrate (C-(A)-S-H) gels and a few crystals. No obvious interfacial transition zones were observed in the microstructure owing to the long-term pozzolanic reaction. This dense and homogenous microstructure was the crucial reason for the excellent long-term performance of the dam concrete. A high FA volume also played a significant role in the microstructural densification and performance growth of dam concrete at a later age. The concrete drilled from the dam surface exhibited a loose microstructure with higher microporosity, indicating that concrete directly exposed to the actual service environment suffered degradation caused by water and wind attacks. In this study, both macro-performance and microstructural analyses revealed that the application of moderate-heat cement and FA resulted in a dense and homogenous microstructure, which ensured the excellent long-term performance of concrete from the Three Gorges Dam after 17 years. Long-term exposure to an actual service environment may lead to microstructural degradation of the concrete surface. Therefore, the retained long-term dam concrete samples need to be further researched to better understand its microstructural evolution and development of its properties.
CO2 is one of the most important greenhouse gases (GHGs) in the earth’s atmosphere. Since the industrial era, anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere, resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events. In 2020, China committed to striving for carbon neutrality by 2060. This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely, comprehensive, and objective monitoring and verification support (MVS) systems. The MVS approach relies on the top-down assimilation and inversion of atmospheric CO2 concentrations, as recommended by the Intergovernmental Panel on Climate Change (IPCC) Inventory Guidelines in 2019. However, the regional high-resolution assimilation and inversion method is still in its initial stage of development. Here, we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition (POD) with four-dimensional variational (4DVar) data assimilation based on the weather research and forecasting-greenhouse gas (WRF-GHG) model. Our China Carbon Monitoring and Verification Support at the Regional level (CCMVS-R) system can continuously assimilate information on atmospheric CO2 and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange. Atmospheric CO2 data were collected from six ground-based monitoring sites in Shanxi Province, China to verify the inversion effect of regional anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method (at 27 and 9 km). The uncertainty of the simulated atmospheric CO2 decreased significantly, with a root-mean-square error of CO2 concentration values between the ideal value and the simulated after assimilation was close to 0. The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6% (17%-38%) higher than the mean of five emission inventories using the bottom-up method, showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.
Copper is a microelement with important physiological functions in the body. However, the excess copper ion (Cu2+) may cause severe health problems, such as hair cell apoptosis and the resultant hearing loss. Therefore, the assay of Cu2+ is important. We integrate ionic imprinting technology (IIT) and structurally colored hydrogel beads to prepare chitosan-based ionically imprinted hydrogel beads (IIHBs) as a low-cost and high-specificity platform for Cu2+ detection. The IIHBs have a macroporous microstructure, uniform size, vivid structural color, and magnetic responsiveness. When incubated in solution, IIHBs recognize Cu2+ and exhibit a reflective peak change, thereby achieving label-free detection. In addition, benefiting from the IIT, the IIHBs display good specificity and selectivity and have an imprinting factor of 19.14 at 100 μmol·L−1. These features indicated that the developed IIHBs are promising candidates for Cu2+ detection, particularly for the prevention of hearing loss.
Skeletal muscle has a robust regeneration ability that is impaired by severe injury, disease, and aging, resulting in a decline in skeletal muscle function. Therefore, improving skeletal muscle regeneration is a key challenge in treating skeletal muscle-related disorders. Owing to their significant role in tissue regeneration, implantation of M2 macrophages (M2Mø) has great potential for improving skeletal muscle regeneration. Here, we present a short-wave infrared (SWIR) fluorescence imaging technique to obtain more in vivo information for an in-depth evaluation of the skeletal muscle regeneration effect after M2Mø transplantation. SWIR fluorescence imaging was employed to track implanted M2Mø in the injured skeletal muscle of mouse models. It is found that the implanted M2Mø accumulated at the injury site for two weeks. Then, SWIR fluorescence imaging of blood vessels showed that M2Mø implantation could improve the relative perfusion ratio on day 5 (1.09 ± 0.09 vs 0.85 ± 0.05; p = 0.01) and day 9 (1.38 ± 0.16 vs 0.95 ± 0.03; p = 0.01) post-injury, as well as augment the degree of skeletal muscle regeneration on day 13 post-injury. Finally, multiple linear regression analyses determined that post-injury time and relative perfusion ratio could be used as predictive indicators to evaluate skeletal muscle regeneration. These results provide more in vivo details about M2Mø in skeletal muscle regeneration and confirm that M2Mø could promote angiogenesis and improve the degree of skeletal muscle repair, which will guide the research and development of M2Mø implantation to improve skeletal muscle regeneration.
