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The main challenge for future autonomous vehicles is to identify their location and body pose in real time during driving, that is, “where am I? and how will I go?”. We address the problems of pose estimation and scene perception from continuous visual frames in intelligent vehicle. Recent advanced technology in the domain of deep learning proposes to train some learning models for vehicle’s series detection tasks in a supervised or unsupervised manner, which has numerous advances over traditional approaches, mainly reflected in the absence of manual calibration and synchronization of the camera and IMU. In the paper, we propose a novel approach for pose estimation and scene recognition with a deep fusion of multi-modal neural features in the manner of unsupervised. Firstly, low-cost camera and IMU are used to extract original visual and inertial data, then the visual and inertial encoders are utilized to encoder the feature of the two modes.

As a critical technology for autonomous vehicles, high precise positioning is essential for automated container terminals to implement intelligent dispatching and to improve container transport efficiency. Because of the unstable performance of global positioning system (GPS) in some circumstances, an ultra wide band (UWB) positioning system is developed for autonomous trucks in an automated container terminal. In this paper, an asynchronous structure is adopted in the system, and a three-dimensional (3D) localization method is proposed. Other than a traditional UWB positioning system with a server, in this asynchronous system, positions are calculated in the vehicle. Therefore, propagation delays from the server to vehicles are eliminated, and the real-time performance can be significantly improved. Traditional 3D localization methods based on time difference of arrival (TDOA) are mostly invalid with anchors in the same plane.

Temporal phase-shifting and spatial-carrier techniques are the two dominant phase extraction methods used in digital speckle pattern interferometry (DSPI). Temporal phase-shifting technique enjoys the advantages of precise phase extraction and capability of high-quality phase map formation, but suffers from the limitation in its use in dynamic measurement due to the time cost for carrying out the phase shift. To meet the rising demand of dynamic measurement of deformations in modern industry, spatial-carrier technique is developed to extract the phase information from a single speckle interferogram, yielding less time cost during a test. In this paper, both temporal phase-shifting and spatial-carrier techniques are investigated in theory as well as experiment. The experiment results from measuring a same deformation behavior using both techniques are used to compare the performances of the two phase extraction techniques.