A Calibration Method for Misalignment Angle of Vehicle-mounted IMU

AbstractIn order to get accurate navigation data via Strapdown Inertial Navigation System (SINS), calibration of misalignment angle between Inertial Measurement Unit (IMU) and vehicle is necessary. The misalignment angle model is simplified, assuming that vehicle travels on a horizontal plane. Due to the velocity of vehicle is small, equation of specific force is simplified. And then equation of misalignment angle is deduced on condition that vehicle travels on a horizontal plane, making straight motion ideally. Angular rate is used to discriminate that vehicle making straight motion. Steps for calibrate misalignment angle are established. In calibration experiment, data are collected and misalignment angle of vehicle-mounted IMU is calibrated using simple techniques of data processing. Analysis shows that, compensation of misalignment angle helps improving the accuracy of SINS. Position error of SINS solution is decreased and the acceleration of vehicle is more accurate

A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

Traffic safety is an important part of the roadway in sustainable development. Freeway traffic crashes typically cause serious casualties and property losses, being a serious threat to public safety. Figuring out the potential correlation between various risk factors and revealing their coupling mechanisms are of effective ways to explore and identity freeway crash causes. However, the existing association rule mining algorithms still have some limitations in both efficiency and accuracy. Based on this consideration, using the freeway traffic crash data obtained from WDOT (Washington Department of Transportation), this research constructed a multi-dimensional multilevel system for traffic crash analysis. Considering the load balancing, the FP-Growth (Frequent Pattern- Growth) algorithm was optimized parallelly based on Hadoop platform, to achieve an efficient and accurate association rule mining calculation for massive amounts of traffic crash data; then, according to the results of the coupling mechanism among the crash precursors, the causes of freeway traffic crashes were identified and revealed. The results show that the parallel FPgrowth algorithm with load balancing constraints has a better operating speed than both the conventional FP-growth algorithm and parallel FP-growth algorithm towards processing big data. This improved algorithm makes full use of Hadoop cluster resources and is more suitable for large traffic crash data sets mining while retaining the original advantages of conventional association rule mining algorithm. In addition, the mining association rules model with the improvement of multi-dimensional interaction proposed in this research can catch the occurrence mechanism of freeway traffic crash with serious consequences (lower support degree probably) accurately and efficiently

Ultra-compact silicon nitride grating coupler for microscopy systems

Grating couplers have been widely used for coupling light between photonic chips and optical fibers. For various quantum-optics and bio-optics experiments, on the other hand, there is a need to achieve good light coupling between photonic chips and microscopy systems. Here, we propose an ultra-compact silicon nitride (SiN) grating coupler optimized for coupling light from a waveguide to a microscopy system. The grating coupler is about 4 by 2 mu m(2) in size and a 116 nm 1 dB bandwidth can be achieved theoretically. An optimized fabrication process was developed to realize suspended SiN waveguides integrated with these couplers on top of a highly reflective bottom mirror. Experimental results show that up to 53% (2.76 dB loss) of the power of the TE mode can be coupled from a suspended SiN waveguide to a microscopy system with a numerical aperture (NA) = 0.65. Simulations show this efficiency can increase up to 75% (1.25 dB loss) for NA = 0.95

Analytical model of spread of epidemics in open finite regions

Epidemic dynamics, a kind of biological mechanisms describing microorganism propagation within populations, can inspire a wide range of novel designs of engineering technologies, such as advanced wireless communication and networking, global immunization on complex systems, and so on. There have been many studies on epidemic spread, but most of them focus on closed regions where the population size is fixed. In this paper, we proposed a susceptible-exposed-infected-recovered model with a variable contact rate to depict the dynamic spread processes of epidemics among heterogeneous individuals in open finite regions. We took the varied number of individuals and the dynamic migration rate into account in the model. We validated the effectiveness of our proposed model by simulating epidemics spread in different scenarios. We found that the average infected possibility of individuals, the population size of infectious individuals in the regions, and the infection ability of epidemics have great impact on the outbreak sizes of epidemics. The results demonstrate that the proposed model can well describe epidemics spread in open finite regions

A Simple Baseline for Supervised Surround-view Depth Estimation

Depth estimation has been widely studied and serves as the fundamental step of 3D perception for autonomous driving. Though significant progress has been made for monocular depth estimation in the past decades, these attempts are mainly conducted on the KITTI benchmark with only front-view cameras, which ignores the correlations across surround-view cameras. In this paper, we propose S3Depth, a Simple Baseline for Supervised Surround-view Depth Estimation, to jointly predict the depth maps across multiple surrounding cameras. Specifically, we employ a global-to-local feature extraction module which combines CNN with transformer layers for enriched representations. Further, the Adjacent-view Attention mechanism is proposed to enable the intra-view and inter-view feature propagation. The former is achieved by the self-attention module within each view, while the latter is realized by the adjacent attention module, which computes the attention across multi-cameras to exchange the multi-scale representations across surround-view feature maps. Extensive experiments show that our method achieves superior performance over existing state-of-the-art methods on both DDAD and nuScenes datasets

Modelling and analyses of helical milling process

A comparison between the geometry of the helical milling specialized tool and conventional end mill was firstly introduced. Furthermore, a mathematical model, in which the cutting area was divided into different cutting zones, was established to simulate the cutting depths and volume of the different cutting edges (three kinds) on specialized tool. Accordingly, a specific ratio between the volume removed by different edges and the total hole volume was derived mathematically and modeled using 3D modeling software SolidWorks. Based on the established models, the cutting depths and cutting volume ratio variation trends under different cutting parameters were analyzed. The results showed that the change rules of cutting depths were different in every cutting zone and influenced greatly by the cutting parameters. In addition, the cutting volume ratio changes with different cutting parameters, but it can only vary in certain range due to the structure of the helical milling specialized tool. The cutting volume ratio obtained from the established model shows a good agreement with the data modeled using SolidWorks, proving that the established model is appropriate. Moreover, the undeformed chip geometry was modeled and observed using SolidWorks. The undeformed chip showed a varying geometry with different cutting parameters and it can be optimized to obtain a good cutting condition during helical milling process

Reliability-oriented optimization of computation offloading for cooperative vehicle-infrastructure systems

Computation offloading is critical for mobile applications that are sensitive to computational power, while dynamic and random nature of vehicular networks makes it challenging to guarantee the reliability of vehicular computation offloading. In this letter, we propose a reliability-oriented stochastic optimization model based on dynamic programming for computation offloading in the presence of the deadline constraint on application execution. Specifically, a theoretical lower bound of the expected reliability of computation offloading is derived, and then an optimal data transmission scheduling mechanism is proposed to maximize the lower bound with consideration of randomness in vehicle-to-infrastructure (V2I) communications. Experimental results demonstrate that our mechanism can outperform the conventional scheme and benefits vehicular computation offloading in terms of reliability performance in stochastic situations

A microbial inspired routing protocol for VANETs

We present a bio-inspired unicast routing protocol for vehicular Ad Hoc Networks which uses the cellular attractor selection mechanism to select next hops. The proposed unicast routing protocol based on attractor selecting (URAS) is an opportunistic routing protocol, which is able to change itself adaptively to the complex and dynamic environment by routing feedback packets. We further employ a multi-attribute decision-making strategy, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), to reduce the number of redundant candidates for next-hop selection, so as to enhance the performance of attractor selection mechanism. Once the routing path is found, URAS maintains the current path or finds another better path adaptively based on the performance of current path, that is, it can self-evolution until the best routing path is found. Our simulation study compares the proposed solution with the state-of-the-art schemes, and shows the robustness and effectiveness of the proposed routing protocol and the significant performance improvement, in terms of packet delivery, end-to-end delay, and congestion, over the conventional method

Channel access optimization with adaptive congestion pricing for cognitive vehicular networks: an evolutionary game approach

Cognitive radio-enabled vehicular nodes as unlicensed users can competitively and opportunistically access the radio spectrum provided by a licensed provider and simultaneously use a dedicated channel for vehicular communications. In such cognitive vehicular networks, channel access optimization plays a key role in making the most of the spectrum resources. In this paper, we present the competition among self-interest-driven vehicular nodes as an evolutionary game and study fundamental properties of the Nash equilibrium and the evolutionary stability. To deal with the inefficiency of the Nash equilibrium, we design a delayed pricing mechanism and propose a discretized replicator dynamics with this pricing mechanism. The strategy adaptation and the channel pricing can be performed in an asynchronous manner, such that vehicular users can obtain the knowledge of the channel prices prior to actually making access decisions. We prove that the Nash equilibrium of the proposed evolutionary dynamics is evolutionary stable and coincides with the social optimum. Besides, performance comparison is also carried out in different environments to demonstrate the effectiveness and advantages of our method over the distributed multi-agent reinforcement learning scheme in current literature in terms of the system convergence, stability and adaptability