Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement Learning Theory

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.

Geoacoustic Inversion for Bottom Parameters via Bayesian Theory in Deep Ocean

We develop a new approach to estimating bottom parameters based on the Bayesian theory in deep ocean. The solution in a Bayesian inversion is characterized by its posterior probability density （PPD）, which combines prior information about the model with information from an observed data set. Bottom parameters are sensitive to the transmission loss （TL） data in shadow zones of deep ocean. In this study, TLs of different frequencies from the South China Sea in the summer of 2014 are used as the observed data sets. The interpretation of the multidimensional PPD requires the calculation of its moments, such as the mean, covariance, and marginal distributions, which provide parameter estimates and uncertainties. Considering that the sensitivities of shallow- zone TLs vary for different frequencies of the bottom parameters in the deep ocean, this research obtains bottom parameters at varying frequencies. Then, the inversion results are compared with the sampling data and the correlations between bottom parameters are determined. Furthermore, we show the inversion results for multi- frequency combined inversion. The inversion results are verified by the experimental TLs and the numerical results, which are calculated using the inverted bottom parameters for different source depths and receiver depths at the corresponding frequency.

An Information Theory for the Geotechnical Engineering Inverse Problem

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A two-dimensional lithospheric magnetic anomaly field model of Egypt using the measurements from Swarm satellites

We use magnetic field data observed by the Swarm mission from 2014 to 2020 to construct,for the first time,a two-dimensional(2 D)lithospheric magnetic anomaly model of Egypt and its surrounding area.Nighttime data during quiet geomagnetic conditions has been expanded in terms of the Legendre polynomial in harmonic terms N=6-50.The damped least square method has been used to estimate the model coefficients based on the lithospheric magnetic data.Modeled data at two different altitudes(438-448 km and 503-511 km)were compared with the CHAOS model.Results exhibit that the 2 D model is superior to the CHAOS model in the capability of extracting more information about small-scale crustal anomaly field.At low altitudes(438-448 km),the strength of the anomaly field increases,but the noise of the external fields has greatly reduced at high altitudes(503-511 km).Besides,the magnetic anomaly field at low altitudes has illuminated short-scale anomalies that didn’t appear at high altitudes.Both the total and vertical magnetic anomaly vectors showed their ability to reveal tectonic structures compared with Moho depth map and the geological maps.

THE STUDY OF RETRIEVAL THEORY AND METHODS FROM SATELLITE REMOTE SENSING FOR METEOROLOGICAL PARAMETERS OVER EASTERN ASIA-PARTI:ISPRM AND SRRM

A review of ten-year's practice in developing the improved simultaneous physical retrieval method(ISPRM)is given in the hope that some creative ideas can be drawn from it.The improvement upon the SPRM is associated with the under-determinedness of this ill-posed inverse problem.In our experiment,the precondition is observed that prior information must be independent of the satellite measurements.The well-posed retrieval theory has told us that the forward process is fundamental for the retrieval,and it is the bridge between the input of satellite radiance and the output of retrievals.In order to obtain a better result from the forward process. the full advantage of every prior information available must be taken.It is necessary to turn the ill- posed inverse problem into the well-posed one.Then by using the Ridge regression or Bayes algorithm to find the optimal combination among the first guess,the theoretical analogue information and the satellite observations,the impact of the under-determinedness of this inverse problem on the numerical solution is minimized.

A New Earthquake Location Method Based on the Waveform Inversion

In this paper,a new earthquake location method based on the waveform inversion is proposed.As is known to all,the waveform misfit function under the L2 measure is suffering from the cycle skipping problem.This leads to a very small convergence domain of the conventional waveform based earthquake location methods.In present study,by introducing and solving two simple sub-optimization problems,we greatly expand the convergence domain of the waveform based earthquake location method.According to a large number of numerical experiments,the new method expands the range of convergence by several tens of times.This allows us to locate the earthquake accurately even from some relatively bad initial values.

THE WASSERSTEIN-FISHER-RAO METRIC FOR WAVEFORM BASED EARTHQUAKE LOCATION

In this paper,we apply the Wasserstein-Fisher-Rao(WFR)metric from the unbalanced optimal transport theory to the earthquake location problem.Compared with the quadratic Wasserstein(W2)metric from the classical optimal transport theory,the advantage of this method is that it retains the important amplitude information as a new constraint,which avoids the problem of the degeneration of the optimization objective function near the real earthquake hypocenter and origin time.As a result,the deviation of the global minimum of the optimization objective function based on the WFR metric from the true solution can be much smaller than the results based on the W2 metric when there exists strong data noise.Thus,we develop an accurate earthquake location method under strong data noise.Many numerical experiments verify our conclusions.