Vehicular ad hoc networks (VANETs) need to support the timely end-to-end transmissions of safety and non-safety messages. Medium access control (MAC) protocols can ensure fair and efficient sharing of channel resources among multiple vehicles for VANETs, which can provide timely packet transmissions and significantly improve road safety. In this paper, we review the standards of some countries for VANETs. Then, we divide the MAC protocols proposed for VANETs into single-channel MAC protocols and multi-channel MAC protocols according to the number of physical occupied spectrum resources. Both are further discussed based on their hierarchical structures, i.e., distributed and centralized structures. General design and optimization mechanisms of these commonly used MAC protocols for VANETs are reviewed. From the viewpoint of 7 aspects, we compare the advantages and disadvantages of these typical MAC protocols. Finally, we discuss the open issues to improve the MAC performance and future work on MAC design for VANETs.

Pedestrian detection is an essential problem of computer vision, which has achieved tremendous success under controllable conditions using visible light imaging sensors in recent years. However, most of them do not consider low-light environments which are very common in real-world applications. In this paper, we propose a novel pedestrian detection algorithm using multi-task learning to address this challenge in low-light environments. Specifically, the proposed multi-task learning method is different from the most commonly used multi-task learning method—the parameter sharing mechanism—in deep learning. We design a novel multi-task learning method with feature-level fusion and a sharing mechanism. The proposed approach contains three parts: an image relighting subnetwork, a pedestrian detection subnetwork, and a feature-level multi-task fusion learning module. The image relighting subnetwork adjusts the low-light image quality for detection, the pedestrian detection subnetwork learns enhanced features for prediction, and the feature-level multi-task fusion learning module fuses and shares features among component networks for boosting image relighting and detection performance simultaneously. Experimental results show that the proposed approach consistently and significantly improves the performance of pedestrian detection on low-light images obtained by visible light imaging sensor.

In M2M networks, most nodes are powered by battery; hence the overused nodes may easily be out of power, which causes the reduction of network lifetime. To solve this problem, it is helpful to balance network load into more nodes and links, so as to reduce network congestion. Multipath routing is a useful tool to reduce congestion, since data flow can be dispersed into multiple paths. However, most of the previous multipath routing algorithm is based on the path disjoint constraint, which leads to the lack of routing paths and the failure to disperse data flow into more links. In this paper, a directed acyclic graph based multipath routing algorithm for congestion minimization (DAGMR) is proposed, where different routing paths are confined in a directed acyclic graph (DAG) under time delay constraint. Furthermore, the data flow distribution is accomplished through partial capacity network to obtain the minimal multipath routing congestion factor. Simulation indicates that our algorithm can obtain lower congestion factor and formulates multipath routing graph with more nodes and links than algorithm with path disjoint constraint. In addition, our algorithm is a polynomial time complexity algorithm with nodes and links number of the entire network.