High-precision lane keeping is essential for the future autonomous driving. However, due to the imbalanced and inaccurate datasets collected by human drivers, current end-to-end driving models have poor lane keeping the effect. To improve the precision of lane keeping, this study presents a novel multi-state model-based end-to-end lane keeping method. First, three driving states will be defined: going straight, turning right and turning left. Second, the finite-state machine (FSM) table as well as three kinds of training datasets will be generated based on the three driving states. Instead of collecting the dataset by human drivers, the accurate dataset will be collected by the high-performance path following controller. Third, three sets of parameters based on 3DCNN-LSTM model will be trained for going straight, turning left and turning right, which will be combined with FSM table to form a multi-state model. This study evaluates the multi-state model by testing it on five tracks and recording the lane keeping error. The result shows the multi-state model-based end-to-end method performs the higher precision of lane keeping than the traditional single end-to-end model.

In this study, a fast object detection algorithm based on binary deep convolution neural networks (CNNs) is proposed. Convolution kernels of different sizes are used to predict classes and bounding boxes of multi-scale objects directly in the last feature map of a deep CNN. In this way, rapid object detection with acceptable precision loss is achieved. In addition, binary quantisation for weight values and input data of each layer is used to squeeze the networks for faster object detection. Compared to full-precision convolution, the proposed binary deep CNNs for object detection results in 62 times faster convolutional operations and 32 times memory saving in theory, what's more, the proposed method is easy to be implemented in embedded computing systems because of the binary operation for convolution and low memory requirement. Experimental results on Pascal VOC2007 validate the effectiveness of the authors’ proposed method.

Feature matching has been frequently applied in computer vision and pattern recognition. In this paper, the authors propose a fast feature matching algorithm for vector-based feature. Their algorithm searches r-nearest neighborhood clusters for the query point after a k-means clustering, which shows higher efficiency in three aspects. First, it does not reformat the data into a complex tree, so it shortens the construction time twice. Second, their algorithm adopts the r-nearest searching strategy to increase the probability to contain the exact nearest neighbor (NN) and take this NN as the global one, which can accelerate the searching speed by 170 times. Third, they set up a matching rule with a variant distance threshold to eliminate wrong matches. Their algorithm has been tested on large SIFT databases with different scales and compared with two widely applied algorithms, priority search km-tree and random kd-tree. The results show that their algorithm outperforms both algorithms in terms of speed up over linear search, and consumes less time than km-tree. Finally, they carry out the CFI test based on ISKLRS database using their algorithm. The test results show that their algorithm can greatly improve the recognition speed without affecting the recognition rate.

Functioning of the Internet is persistently transforming from the Internet of computers (IoC) to the ‘Internet of things (IoT)’. Furthermore, massively interconnected systems, also known as cyber-physical systems (CPSs), are emerging from the assimilation of many facets like infrastructure, embedded devices, smart objects, humans, and physical environments. What the authors are heading to is a huge ‘Internet of Everything in a Smart Cyber Physical Earth’. IoT and CPS conjugated with ‘data science’ may emerge as the next ‘smart revolution’. The concern that arises then is to handle the huge data generated with the much weaker existing computation power. The research in data science and artificial intelligence (AI) has been striving to give an answer to this problem. Thus, IoT with AI can become a huge breakthrough. This is not just about saving money, smart things, reducing human effort, or any trending hype. This is much more than that – easing human life. There are, however, some serious issues like the security concerns and ethical issues which will go on plaguing IoT. The big picture is not how fascinating IoT with AI seems, but how the common people perceive it – a boon, a burden, or a threat.

Person re-identification (Re-ID) is a fundamental subject in the field of the computer vision technologies. The traditional methods of person Re-ID have difficulty in solving the problems of person illumination, occlusion and attitude change under complex background. Meanwhile, the introduction of deep learning opens a new way of person Re-ID research and becomes a hot spot in this field. This study reviews the traditional methods of person Re-ID, then the authors focus on the related papers about different person Re-ID frameworks on the basis of deep learning, and discusses their advantages and disadvantages. Finally, they propose the direction of further research, especially the prospect of person Re-ID methods based on deep learning.

Coarse-to-fine pyramid and scale space are two important image structures in the realm of image matching. However, the advantage of coarse-to-fine pyramid is neglected as the pyramid structure is usually constructed with the down sampling method in scale space. In addition, the importance of each lattice is different for one single image. Based on the analyses above, the new multi-pyramid (M-P) image spatial structure is constructed. First, coarse-to-fine pyramid is constructed by partitioning the original image into increasingly finer lattices, and the number of interest points is also adopted to be each lattice's non-normalized weight on each pyramid level. Second, the scale space of each lattice on each pyramid level is generated with the classic Gaussian kernel. Third, the descriptors of each lattice are generated by regarding the stability of scale space as the description of image. Moreover, the parallel version of M-P algorithm is also presented to accelerate the speed of computation. Finally, the comprehensive experimental results reveal that our multi-pyramid structure which is constructed by the combination of coarse-to-fine spatial pyramid and scale space can generate more effective features, compared with the other related methods.

An adaptive proportional–integral–derivative (PID) controller based on Q-learning algorithm is proposed to balance the cart–pole system in simulation environment. This controller was trained using Q-learning algorithm and implemented the learned Q-tables to change the gains of linear PID controllers according to the state of the system during the control process. The adaptive PID controller based on Q-learning algorithm was trained from a set of fixed initial positions and was able to balance the system starting from a series of initial positions that are different from the ones used in the training session, which achieved equivalent or even better performances in comparison with the conventional PID controller and the controller only uses Q-learning algorithm. This indicates the advantage of the adaptive PID controller based on Q-learning algorithm both in the generality of balancing the cart–pole system from a relatively wide range of initial positions and in the stabilisability of achieving smaller steady-state error.