Real-TimeTrajectoryPlanningforAutonomousUrbanDriving

需积分: 14 185 浏览量 2020-02-26 16:28:36 上传评论收藏 2.67MB PDF 举报

资源推荐

资源详情

资源评论

740 IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 21, NO. 2, APRIL 2016

Real-Time Trajectory Planning for Autonomous

Urban Driving: Framework, Algorithms,

and Veriﬁcations

Xiaohui Li, Zhenping Sun, Dongpu Cao, Zhen He, and Qi Zhu

Abstract

—This paper focuses on the real-time trajectory

planning problem for autonomous vehicles driving in real-

istic urban environments. To solve the complex navigation

problem, we adopt a hierarchical motion planning frame-

work. First, a rough reference path is extracted from the

digital map using commands from the high-level behav-

ioral planner. The conjugate gradient nonlinear optimiza-

tion algorithm and the cubic B-spline curve are employed

to smoothen and interpolate the reference path sequentially.

To follow the reﬁned reference path as well as handle both

static and moving objects, the trajectory planning task is

decoupled into lateral and longitudinal planning problems

within the curvilinear coordinate framework. A rich set of

kinematically feasible path candidates are generated to deal

with the dynamic trafﬁc both deliberatively and reactively. In

the meanwhile, the velocity proﬁle generation is performed

to improve driving safety and comfort. After that, the gen-

erated trajectories are carefully evaluated by an objective

function, which combines behavioral decisions by reason-

ing about the trafﬁc situations. The optimal collision-free,

smooth, and dynamically feasible trajectory is selected and

transformed into commands executed by the low-level lat-

eral and longitudinal controllers. Field experiments have

been carried out with our test autonomous vehicle on the

realistic inner-city roads. The experimental results demon-

strated capabilities and effectiveness of the proposed tra-

jectory planning framework and algorithms to safely handle

a variety of typical driving scenarios, such as static and

moving objects avoidance, lane keeping, and vehicle fol-

lowing, while respecting the trafﬁc rules.

Index Terms

—Autonomous urban driving, real-time

trajectory planning, static obstacles and moving objects

avoidance.

I. INTRODUCTION AND STAT E-OF-THE-ART

UTONOMOUS driving technologies have great potentials

to improve driving safety by reducing trafﬁc accidents and

fatalities caused by human errors, enhance driving efﬁciency by

Manuscript received April 29, 2015; revised August 3, 2015; accepted

October 19, 2015. Date of publication October 26, 2015; date of cur-

rent version February 24, 2016. Recommended by Technical Editor C.

Manzie. This work was supported by the National Nature Science Foun-

dation of China under Grant 90820302.

X. Li, Z. Sun, Z. He, and Q. Zhu are with the College of Mechatronic

Engineering and Automation, National University of Defense Technol-

ogy, Changsha 410073, China (e-mail: xiaohui_lee1986@hotmail.com;

sunzhenping@outlook.com; hezhen.nudt@gmail.com; zhq_cs@126.

com).

D. Cao is with the Department of Automotive Engineering, Cranﬁeld

University, Cranﬁeld MK43 0AL, U.K. (e-mail: d.cao@cranﬁeld.ac.uk).

Color versions of one or more of the ﬁgures in this paper are available

online at http://ieeexplore.ieee.org.

Digital Object Identiﬁer 10.1109/TMECH.2015.2493980

reducing trafﬁc congestion, as well as provide mobility for peo-

ple who are not able to drive [1]–[3]. Fully autonomous driving

is generally identiﬁed as the ultimate goal of driver assistance

systems in the future [4]. The past three decades have witnessed

the signiﬁcant development of autonomous driving technolo-

gies, which have drawn unprecedentedly considerable attention

from both academia and industry. Tremendous research efforts

have been contributed toward the ambitious goal of realizing

fully autonomous driving on realistic roads [5]–[7].

Particularly in the last decade, with the advent of advances

in sensors, computer technologies, and artiﬁcial intelligence,

autonomous-driving-related research topics have become ex-

traordinarily active in both the robotics community and the au-

tomotive industry [8]–[12]. Among these well-known research

projects, the DARPA Urban Challenge in 2007 could be rec-

ognized as a turning point in demonstrating the potentials of

autonomous vehicles driving in urban environments. After that,

research groups in both universities and companies are continu-

ously studying autonomous driving technologies to investigate

necessary technologies for autonomous driving on public roads.

Very recently, there has existed a number of impressive research

projects on testing autonomous vehicles driving in urban and

highway environments [13]–[19].

When autonomous ground vehicles (AGVs) advance toward

the realistic urban road trafﬁc, they are required to be capable

of handling various complex maneuvers, such as lane keep-

ing, vehicle following, lane changing, merging, avoiding both

static and dynamic objects, and interacting with other trafﬁc par-

ticipants while complying with the trafﬁc rules. Developing a

robotic vehicle to have these functionalities requires the system-

atic integration of state-of-the-art technologies in perception,

localization, decision making, motion planning, and control. As

one of these core technologies, motion planning plays a critical

role in guaranteeing driving safety and comfort. In general, the

challenges of developing a reliable and robust on-road motion

planner lie in the following factors: 1) generating dynamically

feasible trajectories in real time with limited onboard compu-

tational resources; 2) dealing with the unpredictably changing

surrounding environments with limited sensing range and vis-

ibility as well as the uncertainty and noise in the perception

and localization systems; 3) interacting with other trafﬁc partic-

ipants, such as vehicles, cyclists, and pedestrians.

Based upon a large amount of previous research on this topic

in the literature, this paper focuses on solving the trajectory

planning problem in urban driving scenarios in a practical way

instead of a theoretical way.

See http://www.ieee.org/publications

standards/publications/rights/index.html for more information.

et al.

: REAL-TIME TRAJECTORY PLANNING FOR AUTONOMOUS URBAN DRIVING 741

A. Related Work

In recent years, a signiﬁcant amount of work has been dedi-

cated to solving the motion planning problem for AGVs. These

approaches can be roughly categorized into two classes. One

focuses on computing collision-free paths using deterministic

graph search, such as Hybrid A* in [20] and [21], state-lattice

approaches in [22] and [23], or random sampling-based algo-

rithms, like rapidly exploring random tree (RRT) [24]. Most of

these graph-search approaches are capable of computing long-

term collision-free paths in cluttered environments and prevent-

ing the vehicle from getting stuck into local minima. These

powerful path-based motion planners are suitable for AGVs

navigating in complex unknown environments at low speeds.

However, most of these search-based algorithms assume that

environments are static in each plan cycle. In addition, the gen-

erated paths are often comprised of concatenated short-term

precomputed motion primitives without considering velocity

planning. As a result, they may easily result in stop-and-redirect

motions. In addition, they are usually too computationally de-

manding to run in real time or react to typical dynamic trafﬁc

situations in urban environments. Therefore, these graph-search

algorithms are not suitable for vehicles driving in urban and

highway environments with moving objects. When vehicles

drive in structured environments, the feasible paths are often

strictly constrained by the geometry of roads, which signiﬁ-

cantly reduces the solution space of drivable paths. However,

due to the presence of dynamic trafﬁc participants, the motion

planner is required to explicitly take both lateral and longitudinal

movements into account.

There has been substantial research on real-time local

trajectory generation for AGVs driving in urban environments.

A well-known and efﬁcient strategy follows a discrete opti-

mization scheme. To generate dynamically feasible trajectory

candidates, a model-predictive motion planner is proposed

in [11]. The control inputs are assumed to be curvature

polynomials, which signiﬁcantly reduce the solution space

and guarantee the expressiveness of the generated trajec-

tories as well. Using the same idea, the approach in [25]

generates a ﬁnite set of nudges and swerves with different

lateral offsets shifting from a reference path. A similar approach

is adopted in [26] considering both lateral and longitudinal

movements within the street-relative coordinate. Considering

both vehicle motion model and the associated closed-loop

feedback control laws, a sampling-based motion planner is

proposed in [27], which ensures that the generated trajectories

could be accurately tracked. A similar strategy is also referred

in [24].

Based on the Boss work in DARPA Urban Challenge [11],

an extended trajectory planner is proposed for highway auto-

mated driving using spatiotemporal lattice conforming to the

road geometry [28]. Graphic processor units (GPUs) are uti-

lized to generate thousands of long-term trajectory candidates

in real time. The motion planner demonstrated its ability to han-

dle imminent situations in structured environments. However,

always simultaneously considering both spatial and temporal

dimensions with high resolution makes the search space pro-

hibitively large. Additionally, it is difﬁcult to ensure the temporal

consistency between consecutive replans. Besides, the approach

assumes that the other trafﬁc participants’ motions could be pre-

cisely predicted within a ﬁnite horizon. Based on his work, to

reduce the computation time while retaining the performance of

the spatiotemporal motion planner, a hierarchical motion plan-

ner is proposed to address the complex motion planning prob-

lems in both urban and highway driving within one framework

[29]–[32].

Instead of applying the discrete optimization scheme, very

recent work in [33] formulated the trajectory planning problem

as a nonlinear optimization problem with a number of inequal-

ity constraints. Extracting a reference corridor using the digital

map, vision-based localization information, and high-level de-

cision process, the trajectory planner explicitly considered hard

constraints imposed by both static obstacle and dynamic traf-

ﬁc participants, as well as the physical constraints, like maxi-

mal steering curvature. The sequential quadratic programming

method is applied to solve the computationally demanding op-

timization problem in the continuous state space.

Most of the previous work does not take uncertainties of the

prediction and interactions with other trafﬁc participants into

account. In [34] and [35], to assess the safety of the planned

paths, uncertainties originating from the measurements and pos-

sible behaviors of other trafﬁc participants are considered in a

stochastic way. Besides, the interaction with other trafﬁc par-

ticipants, as well as the limitation of driving maneuvers due

to the road geometry are also explicitly taken into account. In

[36], a compact representation for the on-road environment, the

dynamic probability drivability map, is presented for predic-

tive lane change and merge driver assistance during highway

and urban driving environments. The uncertainties of both ego-

vehicle and other participants are predicted based on Gaussian

propagation in [37].

B. Contributions and Novelties

Based upon the aforementioned previous work, the main con-

tribution of this paper is the introduction of a practical real-time

trajectory generation framework and algorithms for AGVs driv-

ing in realistic urban environments. To handle various dynamic

urban trafﬁc situations both deliberatively and reactively, we

adopt a “divide-and-conquer” strategy. More precisely, a hi-

erarchical framework is employed. The high-level behavioral

planner is responsible for reasoning about complex dynamic

trafﬁc situations and making deliberate discrete maneuver deci-

sions, such as lane following, lane changing, vehicle following,

overtaking a slow-moving vehicles, and so forth. Using the be-

havioral decision, the trajectory generation algorithm assumes

responsibility for generating dynamically feasible and collision-

free trajectories, which could be easily tracked by low-level

tracking controllers. This paper focuses on investigating the

real-time trajectory generation algorithm.

The rough reference path is extracted from the digital map

using the lane-level accurate localization information via the

LiDAR-based localization method, which is similar to the ap-

proaches proposed in [38] and [39]. To improve driving comfort

决策模块

742 IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 21, NO. 2, APRIL 2016

Fig. 1. Software system architecture for the AGV.

and reduce the control efforts, the conjugate gradient nonlin-

ear optimization algorithm and cubic B-spline algorithms are

employed to smoothen and interpolate the reference path se-

quentially. In realistic urban environments, AGVs are required

to always pay attention to the unpredictably changing trafﬁc

situations, generating dynamically feasible trajectories while

avoiding both static and moving objects in real time. To achieve

this, the trajectory planner is performed to handle the dynamic

trafﬁc situations in a reactive manner by explicitly considering

constraints imposed by the vehicle kinematics and dynamics,

as well as environments. Using the deliberative decisions gen-

erated by the behavioral planner, the trajectory planner is able

to focus on the solution space, where the optimal solution is

mostly like to exist. Besides, aggressive actions could also be

avoided in most nonimminent trafﬁc situations. Then, based

on the curvilinear coordinate framework, the trajectory plan-

ning task is decoupled into spatial path planning and velocity

planning subtasks. Instead of using the optimization scheme to

produce a sole optimal trajectory in each replan cycle, the tra-

jectory planner is capable of generating a rich set of suboptimal

candidates, which could efﬁciently overcome the noise in the

perception and localization systems. Besides, it ensures that the

vehicle is able to safely stop in imminent situations. To guar-

antee driving safety and improve driving comfort, an objective

function with a set of cost terms with deﬁnite physical meanings

is carefully designed to select the best trajectory candidate for

execution.

The remainder of this paper is organized as follows. Section

II describes the system framework. The trajectory planning al-

gorithms is introduced in Section III. Section IV presents the

trajectory evaluation and optimization approach. The experi-

mental results in realistic urban scenarios are discussed in detail

in Section V. Section VI draws conclusions and suggests future

work.

II. S

YSTEM FRAMEWORK

An overview of the software system architecture for the AGV

is outlined in Fig. 1. The system is comprised of a variety of

modules, such as sensors, a digital map, task ﬁles, the perception

Fig. 2. Illustration of perception and localization systems.

and localization systems, task planner, route planner, behavioral

planner, trajectory planner, and tracking controllers, low-level

actuators control, and human machine interface (HMI). Each

module communicates with other modules via an speciﬁc pub-

lish/subscribe message passing protocol based on the Inter Pro-

cess Communication Toolkit.

Sensors, such as LiDARs, radars, and cameras, provide the

sensing information of surrounding environments in real time.

Additionally, other sensors, like the GPS combined with inertial

measurement unit (IMU) and the wheel encoders are used to

obtain the vehicle’s rough localization information. The sens-

ing information is mainly applied for two purposes: one is for

the perception system, such as detecting lane markings, trafﬁc

lights, as well as static and dynamic objects (e.g., pedestrians,

cyclists, and the other vehicles); the other is to realize accu-

rate and robust localization. To achieve lane-level accurate and

reliable localization, many researchers take advantage of map-

ping and localization technologies using GPS, IMU, combined

with online 3-D points data from laser scanners [38], [40], [41]

or camera vision data [42]–[44].

In this paper, online sensing 3-D points data from the HDL-

64E Velodyne LiDAR combining with a high-resolution 3-D

map data recorded by manual driving have been applied to esti-

mate the vehicle’s position and pose in real time. Based on this

localization approach, the rich information of a prior digital map

could be exploited. In practice, we use a manually constructed

detailed digital map, which provides abundant trafﬁc informa-

tion, such as lanes information (e.g., the position, the number,

and the topological relations) and trafﬁc regulations (e.g., speed

limits). The details of the localization and map-constructed ap-

proach are not the main focus of this paper.

The results of perception and localization systems are illus-

trated in Fig. 2, the mother map is created ofﬂine using 3-D

points data of the LiDAR. The cells with magenta color are

online detected obstacles, including both dynamic and static

obstacles. The rectangular block represents the moving object

with arrows indicating its moving direction. The white lines in-

dicate the lanes based on the digital map. In practice, we ﬁnd

that the localization method plays a critical role in guaranteeing

our autonomous vehicle safely driving in the dynamic urban

environments during the day and night, with sun or rain.

cost functions用来评估和挑选最优轨迹

剩余13页未读，继续阅读

评论收藏

内容反馈

linxigjs

粉丝: 345
资源: 12

Real-Time Trajectory Planning for Autonomous Urban Driving_ Fram...

最新资源

Real-Time Trajectory Planning for Autonomous Urban Driving_ Fram...

Trajectory Planning for Automatic Machines and Robots.pdf

Trajectory Planning for Automatic Machines and Robots Springer带书签.rar

Trajectory Planning for Automatic Machines and Robots

Trajectory Planning for Automatic Machines and Robots Springer

A Real-Time Motion Planner with Trajectory Optimization for Autonomous Vehicles

A Practical Trajectory Planning Framework for Autonomous Ground vehicles drving

路径规划论文 --英文

Hybrid Trajectory Planning for Autonomous driving

Shared Cross-Modal Trajectory Prediction for Autonomous Driving

On-Manifold Preintegration for Real-Time Visual-Inertial Odometry.pdf

Trajectory Planning for Robot Manipulators.pdf

7DOF-Trajectory planning_轨迹规划_轨迹__7自由度_trajectory_源码

7DOF-Trajectory planning_轨迹规划_轨迹_trajectoryplanning_7自由度_traject

DMPS-Robotic-Trajectory-planning.rar_dmps_dmps轨迹规划_jerk 轨迹_traje

Bencloucif 等。 - 2019 - Cooperative Trajectory Planning for Hapti

Trajectory Planning for Automatic Machines and Robots 勘误

ITOMP: 动态环境下实时重规划的增量轨迹优化

Office Automatic System

Fast-Planner:一种用于四旋翼飞机的鲁棒高效的轨迹规划器

FAST算法详细翻译

Fassbender 等。 - 2014 - Trajectory planning for car-like robots i

Trajectory-Planning-for-PUMA560-master_轨迹规划_机械臂.zip

最新资源