無人駕駛汽車

·編者按·

無人駕駛汽車是智能汽車的一種，主要依靠車內(nèi)以計(jì)算機(jī)系統(tǒng)為主的智能駕駛儀來實(shí)現(xiàn)無人駕駛。無人駕駛汽車技術(shù)是人工智能技術(shù)與傳統(tǒng)的汽車工業(yè)制造技術(shù)有機(jī)融合的結(jié)果，依靠多種傳感器融合、操作控制、路徑規(guī)劃、計(jì)算數(shù)學(xué)，以及導(dǎo)航定位系統(tǒng)協(xié)作實(shí)現(xiàn)。已經(jīng)成為當(dāng)前科學(xué)研究的重點(diǎn)領(lǐng)域之一。該技術(shù)一般是利用車載傳感器來感知車輛周圍環(huán)境，并根據(jù)感知所獲得的道路、車輛位置和障礙物信息，控制車輛的轉(zhuǎn)向和速度，并進(jìn)行車輛行駛的路徑規(guī)劃。

無人駕駛汽車技術(shù)的核心在于決策系統(tǒng)與感知系統(tǒng)的開發(fā)，目前仍然面臨著安全性不足的問題。迄今為止，無人駕駛汽車還停留在研發(fā)和實(shí)驗(yàn)中，尚未被批準(zhǔn)用作商業(yè)用途和用于私家車，以后可以從對(duì)無人駕駛汽車的可靠性及安全性繼續(xù)進(jìn)行研究和試驗(yàn)；對(duì)無人駕駛汽車的制造成本進(jìn)行突破并向批量生產(chǎn)過渡等方面進(jìn)一步開展研究；同時(shí)協(xié)同相關(guān)法律法規(guī)的建立和完善，并同時(shí)啟迪和教育用戶接受新的駕駛規(guī)范和倫理道德。

本專題得到專家熊光明副教授（北京理工大學(xué)）、王世峰副教授（長春理工大學(xué)）的大力支持。

·熱點(diǎn)數(shù)據(jù)排行·

截至2017年8月21日，中國知網(wǎng)（CNKI）和Web of Science（WOS）的數(shù)據(jù)報(bào)告顯示，以“無人駕駛汽車”等為詞條可以檢索到的期刊文獻(xiàn)分別為2150、2277條，本專題將相關(guān)數(shù)據(jù)按照：研究機(jī)構(gòu)發(fā)文數(shù)、作者發(fā)文數(shù)、期刊發(fā)文數(shù)、被引用頻次進(jìn)行排行，結(jié)果如下。

研究機(jī)構(gòu)發(fā)文數(shù)量排名（CNKI）

研究機(jī)構(gòu)發(fā)文數(shù)量排名（WOS）

作者發(fā)文數(shù)量排名（CNKI）

作者發(fā)文數(shù)量排名（WOS）

期刊發(fā)文數(shù)量排名（CNKI）

期刊發(fā)文數(shù)量排名（WOS）

根據(jù)中國知網(wǎng)（CNKI）數(shù)據(jù)報(bào)告，以“無人駕駛汽車”等為詞條可以檢索到的高被引論文排行結(jié)果如下。

國內(nèi)數(shù)據(jù)庫高被引論文排行

根據(jù)Web of Science統(tǒng)計(jì)數(shù)據(jù)，以“無人駕駛汽車”等為詞條可以檢索到的高被引論文排行結(jié)果如下。

國外數(shù)據(jù)庫高被引論文排行

·經(jīng)典文獻(xiàn)推薦·

基于Web of Science檢索結(jié)果，利用Histcite軟件選取LCS（Local Citation Score，本地引用次數(shù)）TOP 30文獻(xiàn)作為節(jié)點(diǎn)進(jìn)行分析，得到本領(lǐng)域推薦的經(jīng)典文獻(xiàn)如下。

Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized motion planning architecture for dynamical systems in the presence of fixed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle’s motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. Simulation examples involving a ground robot and a small autonomous helicopter, are presented and discussed.

來源出版物：American Control Conference , 2002 , 25(1) :43-49

Real-time motion planning with applications to autonomous urban driving

Yoshiaki, K; Justin, T; Gaston, F; et al.

Abstract:This paper describes a real-time motion planning algorithm, based on the rapidly-exploring random tree(RRT) approach, applicable to autonomous vehicles operating in an urban environment. Extensions to the standard RRT are predominantly motivated by: 1) the need to generate dynamically feasible plans in real-time; 2)safety requirements; 3) the constraints dictated by the uncertain operating (urban) environment. The primary novelty is in the use of closed-loop prediction in the framework of RRT. The proposed algorithm was at the core of the planning and control software for Team MIT’s entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles.

來源出版物：IEEE Transactions on Control Systems Technology, 2009, 17(5): 1105-1118

Sampling-based algorithms for optimal motion planning

Sertac, K; Emilio, F

Abstract:During the last decade, sampling-based path planning algorithms, such as probabilistic roadmaps (PRM)and rapidly exploring random trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However,little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g. as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned bystochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g. showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value.The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e. such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.

Stanley: The robot that won the DARPA Grand Challenge

Sebastian, T; Mike, M; Hendrik, D; et al.

Abstract:This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without manual intervention.The robot’s software system relied predominately on state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This paper describes the major components of this architecture, and discusses the results of the Grand Challenge race.

來源出版物：Journal of Field Robotics, 2006, 23(9): 661-692

A perception-driven autonomous urban vehicle

John, L; Jonathan, H; Seth, T; et al.

Abstract:This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant

來源出版物：The International Journal of Robotics Research,2011, 30(7): 846-894 communications and computation bandwidth to capture and process high-resolution, high-rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kinodynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three-point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed-loop simulation in a rapidly exploring randomized trees algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in global positioning system–denied and highly dynamic environments with poor a priori information.

來源出版物：Journal of Field Robotics, 2008, 25(10):727-774

Autonomous driving in urban environments: Boss and the Urban Challenge

Chris, U; Joshua, A; Drew, B; et al.

Abstract:Boss is an autonomous vehicle that uses on-board sensors (global positioning system, lasers, radars,and cameras) to track other vehicles, detect static obstacles,and localize itself relative to a road model. A three-layer planning system combines mission, behavioral, and motion planning to drive in urban environments. The mission planning layer considers which street to take to achieve a mission goal. The behavioral layer determines when to change lanes and precedence at intersections and performs error recovery maneuvers. The motion planning layer selects actions to avoid obstacles while making progress toward local goals. The system was developed from the ground up to address the requirements of the DARPA Urban Challenge using a spiral system development process with a heavy emphasis on regular, regressive system testing. During the National Qualification Event and the 85-km Urban Challenge Final Event, Boss demonstrated some of its capabilities, qualifying first and winning the challenge.

來源出版物：Journal of Field Robotics, 2008, 25(8): 425-466

文章題目第一作者來源出版物1 Real-time motion planning with applications to Yoshiaki, K IEEE Transactions on Control Systems autonomous urban driving Technology, 2009, 17(5): 1105-1118 2 Sampling-based algorithms for optimal motion Sertac, K The International Journal of Robotics Research,planning 2011, 30(7): 846-894 3 Stanley: The robot that won the DARPA Grand Sebastian, T Journal of Field Robotics, 2006, 23(9): 661-692 Challenge 4 A perception-driven autonomous urban vehicle John, L Journal of Field Robotics, 2008, 25(10): 727-774 5 Autonomous driving in urban environments: Boss and Chris, U Journal of Field Robotics, 2008, 25(8): 425-466 the Urban Challenge

Real-time motion planning for agile autonomous vehicles

Frazzoli, E; Dahleh, MA; Feron, E