An Efficient V2X Based Vehicle Localization Using Single RSU and Single Receiver

High accuracy vehicle localization information is critical for intelligent transportation systems and future autonomous vehicles. It is challenging to achieve the required centimeter-level localization accuracy, especially in urban or global navigation satellite system denied environments. Here we propose a vehicle-to-infrastructure (V2I)-based vehicle localization algorithm. First, it is low-cost and hardware requirements are simplified, the minimum requirement is a single roadside unit and single on-board receiver. Second, it is computationally efficient, the available V2I information is formulated as an over-determined system. Then, the vehicle position is estimated in a closed-form manner via the widely used weighted linear least squares (WLLS) method and meter level accuracy is achievable. Furthermore, the numerical performance of WLLS is consistent with the theoretical results in larger signal-to-noise ratio region

Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles

Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle managers, e.g., government or industrial companies, still need a way to tell these self-driving vehicles what behaviors are encouraged or forbidden. Unlike human drivers, current self-driving vehicles cannot understand the traffic laws, thus rely on the programmers manually writing the corresponding principles into the driving systems. It would be less efficient and hard to adapt some temporary traffic laws, especially when the vehicles use data-driven decision-making algorithms. Besides, current self-driving vehicle systems rarely take traffic law modification into consideration. This work aims to design a road traffic law adaptive decision-making method. The decision-making algorithm is designed based on reinforcement learning, in which the traffic rules are usually implicitly coded in deep neural networks. The main idea is to supply the adaptability to traffic laws of self-driving vehicles by a law-adaptive backup policy. In this work, the natural language-based traffic laws are first translated into a logical expression by the Linear Temporal Logic method. Then, the system will try to monitor in advance whether the self-driving vehicle may break the traffic laws by designing a long-term RL action space. Finally, a sample-based planning method will re-plan the trajectory when the vehicle may break the traffic rules. The method is validated in a Beijing Winter Olympic Lane scenario and an overtaking case, built in CARLA simulator. The results show that by adopting this method, the self-driving vehicles can comply with new issued or updated traffic laws effectively. This method helps self-driving vehicles governed by digital traffic laws, which is necessary for the wide adoption of autonomous driving

A survey on 5G massive MIMO Localization

Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field

MMW Radar-Based Technologies in Autonomous Driving: A Review

With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies

Design of a Multi-layer Lane-Level Map for Vehicle Route Planning

With the development of intelligent transportation system, there occurs further demand for high precision localization and route planning, and simultaneously the traditional road-level map fails to meet with this requirement, by which this paper is motivated. In this paper, t he three-layer lane-level map architecture for vehicle path guidance is established, and the mathematical models of road-level layer, intermediate layer and lane-level layer are designed considering efficiency and precision. The geometric model of the lane-level layer of the map is characterized by Cubic Hermite Spline for continuity. A method of generating the lane geometry with fixed and variable control points is proposed, which can effectively ensure the accuracy with limited num ber of control points. In experimental part, a multi-layer map of an intersection is built to validate the map model, and an example of a local map was generated with the lane-level geometry