Road accidents impose serious problems on society. Possible collisions between vehicles and pedestrians must be detected before they occur so that a timely warning may be issued. By using the vision‐based approach, this study presents an effective and efficient algorithm to estimate the vehicle–pedestrian collision probability at intersections. The real‐time trajectories and movement parameters (position, speed, acceleration or direction) of vehicles and pedestrians are obtained based on state‐of‐the‐art detection and tracking algorithm which include background subtraction method, faster regions with convolutional neural networks and optical flow method. To find the appropriate time to identify the latent collision risk for calculating the collision probability, this study defines the critical time based on different collision patterns of perception‐reaction failure and evasive action failure. In addition, based on discrete acceleration and discrete angle, the authors get different extended trajectories which can include most situation when the conflict happened. Trajectories generation probability are given by the discrete choice probability model based on the Logit model to get the accurate collision probability. Real‐world video data is implemented to demonstrate the approach. This proposed collision prediction method can provide some important results for designing the intelligent pedestrian signal timing schemes at intersections.
The performance of the cyclic redundancy check aided successive cancellation list (CA-SCL) decoder for polar codes exceeds that of the Turbo codes and the Low Density Parity Check (LDPC) codes adopted in the World Interoperability for Microwave Access (WiMAX) proposal. However, CA-SCL decoder has a high computational complexity, resulting in a long time delay and high memory complexity. In order to alleviate this problem, an improved path splitting strategy on successive cancellation list (IPSS-SCL) decoder was proposed, which can significantly reduce the average list size. The influence of splitting on CA-SCL decoder was analysed at first and a new selective splitting criterion using the Gaussian approximation method was proposed according to the analysis. In addition, a path contraction mechanism based on the location estimation of the correct path was proposed to further reduce the average number of unnecessary candidate paths. Compared with existing path splitting strategies, simulation results show that the proposed IPSS-SCL decoding algorithm can reduce the average computational complexity significantly over the additive white Gaussian noise (AWGN) channel with almost no performance loss. 1 INTRODUCTION Polar codes [1] is a major area of interest within the field of channel coding recently for it has been proven to be the capacity achieving code for binary-input discrete memoryless channels (BI-DMC). Successive cancellation (SC) decoding scheme [1] is proposed as the original Polar decoding scheme with low complexity and it could approach asymptotically capacity achieving over the BI-DMC when the block length goes infinity. Unfortunately, the main challenge faced by many researchers is that the performance of SC decoder is dissatisfactory in the practical cases with finite-length blocks. To improve it, a successive cancellation list (SC-List) decoding algorithm [2] approaches the performance of maximum likelihood decoding in the high signal-to-noise ratio (SNR) regime. Furthermore, a cyclic redundancy check (CRC) aided successive cancellation list (CA-SCL) [2] decoding algorithm is comparable with the performance of current state-of-the-art LDPC codes. Considering the computational complexity of CA-SCL algorithm, O(LN log N), an adaptive CA-SCL decoding algorithm which uses adaptive list size L instead of constant L for decoding [3]. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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