Networking and Communications in Autonomous Driving: A Survey

IEEE Trans. Veh. Technol.

et al. 2020

This paper considers a rotary-wing unmanned aerial vehicle (UAV)-enabled wireless power transfer system, where a UAV is dispatched as an energy transmitter (ET), transferring radio frequency (RF) signals to a set of energy receivers (ERs) periodically. We aim to maximize the energy harvested at all ERs by jointly optimizing the UAV's three-dimensional (3D) placement, beam pattern and charging time. However, the considered optimization problem taking into account the drone flight altitude and the wireless coverage performance is formulated as a non-convex problem. To tackle this problem, we propose a low-complexity iterative algorithm to decompose the original problem into four sub-problems in order to optimize the variables sequentially. In particular, we first use the sequential unconstrained convex minimization based algorithm to find the globally optimal UAV two-dimensional (2D) position. Subsequently, we can directly obtain the optimal UAV altitude as the objective function of problem is monotonic decreasing with respect to UAV altitude. Then, we propose the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm to control the phase of antenna array elements, in order to achieve high steering performance of multi-beams. Finally, with the above solved variables, the original problem is reformulated as a single-variable optimization problem where charging time is the optimization variable, and can be solved using the standard convex optimization techniques. Furthermore, we use the branch and bound method to design the UAV trajectory which can be constructed as traveling salesman problem (TSP) to minimize flight distance. Numerical results validate the theoretical findings and demonstrate that significant performance gain in terms of sum received power of ERs can be achieved by the proposed algorithm in UAV-enabled wireless power transfer networks.Index Terms-Multi-beam, trajectory optimization, UAV 3D placement, wireless power transfer.

Section: Introductionmentioning

confidence: 99%

Joint 3D Trajectory Design and Time Allocation for UAV-Enabled Wireless Power Transfer Networks

Feng

IEEE Trans. Veh. Technol.

et al. 2020

“…Furthermore, in order to achieve the ultimate goal of autonomous driving on the road, future autonomous vehicles need sophisticated sensors to support safety-related applications. Ultrareliable and low-latency communications (URLLCs) exchanging near-real-time information are also required to offer assistance to control systems [4], [5]. To this end, it is paramount to conceive URLLC techniques for future VNETs.…”

Section: Introductionmentioning

confidence: 99%

Twin-Timescale Radio Resource Management for Ultra-Reliable and Low-Latency Vehicular Networks

Yang

Zheng

IEEE Trans. Veh. Technol.

et al. 2020

To efficiently support safety-related vehicular applications, the ultra-reliable and low-latency communication (URLLC) concept has become an indispensable component of vehicular networks (VNETs).Due to the high mobility of VNETs, exchanging near-instantaneous channel state information (CSI) and making reliable resource allocation decisions based on such short-term CSI evaluations are not practical.In this paper, we consider the downlink of a vehicle-to-infrastructure (V2I) system conceived for URLLC based on idealized perfect and realistic imperfect CSI. By exploiting the benefits of the massive MIMO concept, a two-stage radio resource allocation problem is formulated based on a novel twin-timescale perspective for avoiding the frequent exchange of near-instantaneous CSI. Specifically, based on the prevalent road-traffic density, Stage 1 is constructed for minimizing the worst-case transmission latency on a long-term timescale. In Stage 2, the base station allocates the total power at a short-term timescale according to the large-scale fading CSI encountered for minimizing the maximum transmission latency across all vehicular users. Then, a primary algorithm and a secondary algorithm are conceived for our V2I URLLC system to find the optimal solution of the twin-timescale resource allocation problem, with special emphasis on the complexity imposed. Finally, our simulation results show that the proposed resource allocation scheme significantly reduces the maximum transmission latency, and it is not sensitive to the fluctuation of road-traffic density. Index TermsVehicular networks (VNET), ultra-reliable and low-latency communications (URLLC), resource management, finite blocklength theory, massive MIMO.

“…Recently, with the development of V2I technology [3][4][5] and wide-area radar technology [6,7], the low-latency, highprecision, and traceable techniques provide new ideas for the recognition of intersection traffic conditions, especially in judging the truth or falseness of intersection overflow. Although these new technologies have been widely used in traffic control [8][9][10], traffic guidance [11,12], and autonomous driving [13][14][15], no one has attempted to use them in the field of intersection overflow. e segment within a certain range of the upstream intersection is regarded as the research object.…”

Section: Introductionmentioning

confidence: 99%

Research on Intersection Frequent Overflow Control Strategy Based on Wide-Area Radar Data

Zhang

Journal of Advanced Transportation

Wang

2020

Overflow identification and control at urban road intersection is a derivative problem of oversaturation control. The existing cross-section detector uses the road cross-section occupation of vehicles to identify the overflow state; then, the downstream road condition is often ignored. At present, more and more advanced traffic detection technologies, such as vehicle-to-infrastructure and wide-area radar, can provide reliable data for accurate overflow identification. In this paper, a new method of overflow identification and control at intersections is proposed by using advanced wide-area radar detection data. As a detector for specific segment of road, the wide-area radar can detect the traffic flow data in a certain range of road and provide more data types. Therefore, the average speed and space occupancy of the effective detection road segment are selected as subindicators to establish the comprehensive identification index of overflow identification. Then, the overflow control strategy is developed considering the traffic demand of the overflow phase and the nonoverflow phase. It is proved that the method is more accurate and effective in overflow identification and control by using simulation experiment of field data.