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LiDAR-camera fusion is one of the core processes for the perception system of current automated driving systems. The typical sensor fusion process includes a list of coordinate transformation operations following system calibration. Although a significant amount of research has been done to improve the fusion accuracy, there are still inherent data mapping errors in practice related to system synchronization offsets, vehicle vibrations, the small size of the target, and fast relative moving speeds. Moreover, more and more complicated algorithms to improve fusion accuracy can overwhelm the onboard computational resources, limiting the actual implementation. This study proposes a novel and low-cost probabilistic LiDAR-Camera fusion method to alleviate these inherent mapping errors in scene reconstruction. By calculating shape similarity using KL-divergence and applying RANSAC-regression-based trajectory smoother, the effects of LiDAR-camera mapping errors are minimized in object localization and distance estimation. Designed experiments are conducted to prove the robustness and effectiveness of the proposed strategy.

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced LSTM network integrated with a state refinement module. This proactive model is designed to ensure unobstructed movement in areas with substantial pedestrian traffic without collisions. Results are validated on two public datasets, ETH and UCY, providing encouraging outcomes. Our model demonstrated proficiency in anticipating pedestrian paths and augmented scooter path planning, allowing for heightened adaptability in densely populated locales. This study shows the potential of melding pedestrian trajectory prediction with scooter motion planning. With the ubiquity of electric scooters in urban environments, such advancements have become crucial to safeguard all participants in urban transit., Comment: Submitted to SAE 2024

Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, it is impossible to develop a perfectly precise vehicle model when the vehicle is driving. Most PFCs did not consider uncertainties in the vehicle model, external disturbances, and parameter variations at the same time. To address the issues associated with this important feature and function in autonomous driving, a new vehicle PFC is proposed using a robust model predictive control (MPC) design technique based on matrix inequality and the theoretical approach of the hybrid $\&$ switched system. The proposed methodology requires a combination of continuous and discrete states, e.g. regulating the continuous states of the AV (e.g., velocity and yaw angle) and discrete switching of the control strategy that affects the dynamic behaviors of the AV under different driving speeds. Firstly, considering bounded model uncertainties, and norm-bounded external disturbances, the system states and control matrices are modified.

Road departure detection systems (RDDSs) for eliminating unintentional road departure collisions have been developed and equipped on some commercial vehicles in recent years. In order to provide a standardized and objective performance evaluation of RDDSs without the affections of systems complex nature of RDDSs and the design requirements, this paper proposes the development of the scoring method for evaluating vehicle RDDSs. Both flat road edge and vertical road edge are considered in the proposed scoring method, which combines two key variables: 1) the lateral distance of vehicle from road edge when RDW triggers; 2) the lateral distance of vehicle from road edge when RKA triggers. Two main criteria of road departure warning (RDW) and Road Keeping Assistance (RKA) are used to describe the performance of RDDSs.

As one of the most popular micro-mobility options, e-scooters are spreading in hundreds of big cities and college towns in the US and worldwide. In the meantime, e-scooters are also posing new challenges to traffic safety. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than the pedestrains and bicyclists. These features make e-scooters challenging for human drivers, pedestrians, vehicle active safety modules, and self-driving modules to see and interact. To study this new mobility option and address e-scooter riders' and other road users' safety concerns, this paper proposes a wearable data collection system for investigating the micro-level e-Scooter motion behavior in a Naturalistic road environment. An e-Scooter-based data acquisition system has been developed by integrating LiDAR, cameras, and GPS using the robot operating system (ROS). Software frameworks are developed to support hardware interfaces, sensor operation, sensor synchronization, and data saving. The integrated system can collect data continuously for hours, meeting all the requirements including calibration accuracy and capability of collecting the vehicle and e-Scooter encountering data., Comment: Conference: Fast-zero'21, Kanazawa, Japan Date of publication: Sep 2021 Publisher: JSAE

In this paper, the soft gluon resummation effect in double heavy quarkonium production at the LHC is studied. By applying the transverse momentum dependent factorization formalism, the large logarithms introduced by the small total transverse momentum of heavy quarkonium pair final state system, are resummed to all orders in the expansion of the strong interaction coupling at the Next-to-Leading Logarithm accuracy. We also compare our result with the LHC data. We find that the distribution shape predicted by resummation calculation is consistent with experimental data very well. Since this process is mainly initiated by gluon fusion, it supplies us an important channel to study the gluon parton property in hadrons., Comment: 7pages,3figures

Motivated by very recent measurement of the radiative decays of $\Upsilon(1S)$ to $\chi_{c1}$ at Belle, we use the nonrelativistic QCD factorization theory and calculate the branching fractions of the radiative decays of bottomonium into S-wave and P-wave charmonium, i.e. $\Upsilon(nS) \to \eta_c(nS)+\gamma$ and $\Upsilon(nS)\to \chi_{cJ}+\gamma$. We systematically studied the branching fractions of the radiative decays of bottomonium into charmonium. Compared to the previous calculation, we obtained the analytical expression for the decay widths and considered the color-octet contributions. For $\Upsilon(nS) \to \eta_c(nS)+\gamma$, the relativistic corrections are also obtained. Through the calculation, the theoretical prediction for $\Upsilon(1S)\to\chi_{c1}+\gamma$ is still smaller than the recent Belle measurement. Further theoretical work and experimental analysis are necessary to understand the $\chi_{c1}$ production mechanism in upsilon decays., Comment: 10 pages,3 figures,6 tables

This paper presents a combined power system with a common dc bus that contains solar power, wind power, battery storage, and a constant dc load (CDL). In wind system, an AC-DC uncontrolled rectifier is used at the first stage and the DC-DC converter is controlled by a maximum power point tracker (MPPT) at second stage. In the solar system, two cascaded boost converters are controlled through a sliding mode controller (SMC) to regulate the power flow to the load. A supervisory control strategy is also introduced to maximize the simultaneous energy harvesting from both renewable sources and balance the energy between the sources, battery, and the load. According to the level of power generation available at each renewable energy source, the state of charge in the battery, and the load requirement, the controller results in four contingencies. Simulation results show the accurate operation of the supervisory controller and functionality of the maximum power point tracking algorithm for solar and for wind power., Comment: Conference: 41st Annual Conference of the IEEE Industrial Electronics Society, 2015 at Yokohama

Advancement in artificial intelligence (AI) and machine learning (ML), dynamic data driven application systems (DDDAS), and hierarchical cloud-fog-edge computing paradigm provide opportunities for enhancing multi-domain systems performance. As one example that represents multi-domain scenario, a "fly-by-feel" system utilizes DDDAS framework to support autonomous operations and improve maneuverability, safety and fuel efficiency. The DDDAS "fly-by-feel" avionics system can enhance multi-domain coordination to support domain specific operations. However, conventional enabling technologies rely on a centralized manner for data aggregation, sharing and security policy enforcement, and it incurs critical issues related to bottleneck of performance, data provenance and consistency. Inspired by the containerized microservices and blockchain technology, this paper introduces BLEM, a hybrid BLockchain-Enabled secure Microservices fabric to support decentralized, secure and efficient data fusion and multi-domain operations for avionics systems. Leveraging the fine-granularity and loose-coupling features of the microservices architecture, multidomain operations and security functionalities are decoupled into multiple containerized microservices. A hybrid blockchain fabric based on two-level committee consensus protocols is proposed to enable decentralized security architecture and support immutability, auditability and traceability for data provenience in existing multi-domain avionics system. Our evaluation results show the feasibility of the proposed BLEM mechanism to support decentralized security service and guarantee immutability, auditability and traceability for data provenience across domain boundaries., Comment: 2020 SPIE Defense + Commercial Sensing