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Baidu Apollo EM Motion Planner

Haoyang Fan

1,†

, Fan Zhu

2,†

, Changchun Liu, Liangliang Zhang, Li Zhuang,

Dong Li, Weicheng Zhu, Jiangtao Hu, Hongye Li, Qi Kong

3,∗

Abstract— In this manuscript, we introduce a real-

time motion planning system based on the Baidu

Apollo (open source) autonomous driving platform.

The developed system aims to address the industrial

level-4 motion planning problem while considering

safety, comfort and scalability. The system covers

multilane and single-lane autonomous driving in a

hierarchical manner: (1) The top layer of the system

is a multilane strategy that handles lane-change sce-

narios by comparing lane-level trajectories computed

in parallel. (2) Inside the lane-level trajectory gener-

ator, it iteratively solves path and speed optimization

based on a Frenet frame. (3) For path and speed

optimization, a combination of dynamic programming

and spline-based quadratic programming is proposed

to construct a scalable and easy-to-tune framework

to handle trafﬁc rules, obstacle decisions and smooth-

ness simultaneously. The planner is scalable to both

highway and lower-speed city driving scenarios. We

also demonstrate the algorithm through scenario

illustrations and on-road test results.

The system described in this manuscript has been

deployed to dozens of Baidu Apollo autonomous

driving vehicles since Apollo v1.5 was announced in

September 2017. As of May 16th, 2018, the system

has been tested under 3,380 hours and approximately

68,000 kilometers (42,253 miles) of closed-loop au-

tonomous driving under various urban scenarios.

The algorithm described in this manuscript

is available at https://github.com/

ApolloAuto/apollo/tree/master/modules/

planning.

I. INTRODUCTION

Autonomous driving research began in the 1980s

and has signiﬁcantly grown over the past ten years.

Autonomous driving aims to reduce road fatalities,

†

Authors who contributed equally in this manuscript

∗

Corresponding author for this manuscript

www.apollo.auto

Haoyang Fan is with Baidu USA LLC,

fanhaoyang01@baidu.com.

Fan Zhu is with Baidu USA LLC, fanzhu@baidu.com.

Qi Kong is with Baidu USA LLC, kongqi02@baidu.com.

increase trafﬁc efﬁciency and provide convenient

travel. However, autonomous driving is a chal-

lenging task that requires accurately sensing the

environment, a deep understanding of vehicle inten-

tions and safe driving under different scenarios. To

address these difﬁculties, we constructed an Apollo

open source autonomous driving platform. The

ﬂexible modularized architecture of the developed

platform supports fully autonomous driving deploy-

ment https://github.com/ApolloAuto/

apollo.

In the ﬁgure, the HD map module provides a

high-deﬁnition map that can be accessed by every

on-line module. Perception and localization mod-

ules provide the necessary dynamic environment

information, which can be further used to predict

future environment status in the prediction module.

The motion planning module considers all infor-

mation to generate a safe and smooth trajectory to

feed into the vehicle control module.

In motion planner, safety is always the top pri-

ority. We consider autonomous driving safety in,

but not limited to, the following aspects: trafﬁc

regulations, range coverage, cycle time efﬁciency

and emergency safety. All these aspects are critical.

Trafﬁc regulations are designed by governments for

public transportation safety, and such regulations

also apply to autonomous driving vehicles. An

autonomous driving vehicle should follow trafﬁc

regulations at all times. For range coverage, we aim

to provide a trajectory with at least an eight second

or two hundred meter motion planning trajectory.

The reason is to leave enough room to maintain safe

driving within regular autonomous driving vehicle

dynamics. The execution time of the motion plan-

ning algorithm is also important. In the case of an

emergency, the system could react within 100 ms,

compared with a 300 ms reaction time for a normal

arXiv:1807.08048v1 [cs.RO] 20 Jul 2018

human driver. A safety emergency module is the

last shell for protecting the safety of riders. For

a level-4 motion planner, once upstream modules

can no longer function normally, the safety module

within the motion planner shall respond with an

immediate emergency behavior and send warnings

that interrupt humans. Moreover, the autonomous

driving system shall have the ability to react to

emergencies in a lower-level-like control module.

Further safety design is beyond this manuscript’s

scope.

Fig. 1 shows the architecture of the Apollo online

modules.

HD Map

Perception

Localization

Routing

Prediction

Motion

Planning

Vehicle

Control

Apollo Realtime Module Pipeline

Fig. 1: On board modules of the Apollo open source

autonomous driving platform

In addition to safety, passengers’ ride experience

is also important. The measurement of ride experi-

ence includes, but is not limited to, scenario cov-

erage, trafﬁc regulation and comfort. For scenario

coverage, the motion planner should not only be

able to handle simple driving scenarios (e.g., stop,

nudge, yield and overtake) but also handle multi-

lane driving, heavy trafﬁc and other complicated

on-road driving scenarios. Planning within trafﬁc

regulations is also important for ride experience. In

addition to being a safety requirement, following

the trafﬁc regulations will also minimize the risk of

accidents and reduce emergency reactions for au-

tonomous driving. The comfort during autonomous

driving is also important. In motion planning, com-

fort is generally measured by the smoothness of the

provided autonomous driving trajectory.

This manuscript presents the Apollo EM planner,

which is based on an EM-type iterative algorithm

([1]). This planner targets safety and ride experi-

ence with a multilane, path-speed iterative, trafﬁc

rule and decision combined design. The intuitions

for this planner are introduced as follows:

A. Multilane Strategy

For level-4 on-road autonomous driving motion

planning, a lane-change strategy is necessary. One

common approach is to develop a search algorithm

with a cost functional on all possible lanes and then

select a ﬁnal trajectory from all the candidates with

the lowest cost [2], [3]. This approach has some

difﬁculties. First, the search space is expanded

across multiple lanes, which causes the algorithm

to be computationally expensive. Second, trafﬁc

regulations (e.g., right of the road, trafﬁc lights)

are different across lanes, and it is not easy to

apply trafﬁc regulations under the same frame.

Furthermore, trajectory stability that avoids sudden

changes between cycles should be taken into con-

sideration. It is important to follow consistent on-

road driving behavior to inform other drivers of the

intention of the autonomous driving vehicle.

Typically, a multilane strategy should cover both

nonpassive and passive lane-change scenarios. In

EM planner, a nonpassive lane-change is a request

triggered by the routing module for the purpose

of reaching the ﬁnal destination. A passive lane

change is deﬁned as an ego car maneuver when

the default lane is blocked by the dynamic en-

vironment. In both passive and nonpassive lane

changes, we aim to deliver a safe and smooth lane-

change strategy with a high success rate. Thus,

we propose a parallel framework to handle both

passive and nonpassive lane changes. For candidate

lanes, all obstacles and environment information are

projected on lane-based Frenet frames. Then, the

trafﬁc regulations are bound with the given lane-

level strategy. Under this framework, each candi-

date lane will generate a best-possible trajectory

based on the lane-level optimizer. Finally, a cross-

lane trajectory decider will determine which lane to

choose based on both the cost functional and safety

rules.

B. Path-Speed Iterative Algorithm

In lane-level motion planning, optimality and

time consumption are both important. Thus, many

autonomous driving motion planning algorithms

are developed in Frenet frames with time (SLT)

to reduce the planning dimension with the help

of a reference line. Finding the optimal trajectory

in a Frenet frame is essentially a 3D constrained

optimization problem. There are typically two types

of approaches: direct 3D optimization methods and

the path-speed decoupled method. Direct methods

(e.g., [4] and [5]) attempt to ﬁnd the optimal tra-

jectory within SLT using either trajectory sampling

or lattice search. These approaches are limited by

their search complexity, which increases as both

the spatial and temporal search resolutions increase.

To qualify the time consumption requirement, one

has to compromise with increasing the search grid

size or sampling resolution. Thus, the generated

trajectory is suboptimal. Conversely, the path-speed

decoupled approach optimizes path and speed sep-

arately. Path optimization typically considers static

obstacles. Then, the speed proﬁle is created based

on the generated path [6]. It is possible that the

path-speed approach is not optimal with the ap-

pearance of dynamic obstacles. However, since

the path and speed are decoupled, this approach

achieves more ﬂexibility in both path and speed

optimization.

EM planner optimizes path and speed iteratively.

The speed proﬁle from the last cycle is used to

estimate interactions with oncoming and low-speed

dynamic obstacles in the path optimizer. Then, the

generated path is sent to the speed optimizer to

evaluate an optimal speed proﬁle. For high-speed

dynamic obstacles, EM planner prefers a lane-

change maneuver rather than nudging for safety

reasons. Thus, the iterative strategy of EM planner

can help to address dynamic obstacles under the

path-speed decoupled framework.

C. Decisions and Trafﬁc Regulations

In EM planner, decisions and trafﬁc regulations

are two different constraints. Trafﬁc regulations are

a non-negotiable hard constraint, whereas obstacle

yield, overtake, and nudge decisions are negotiable

based on different scenarios. For the decision-

making module, some planners directly apply nu-

merical optimization [7], [5] to make decisions

and plans simultaneously. In Apollo EM planner,

we make decisions prior to providing a smooth

trajectory. The decision process is designed to make

on-road intentions clear and reduce the search space

for ﬁnding the optimal trajectory. Many decision-

included planners attempt to generate vehicle states

as the ego car decision. These approaches can be

further divided into hand-tuning decisions ([8], [9],

[3]) and model-based decisions ([10], [11]). The

advantage of hand-tuning decision is its tunability.

However, scalability is its limitation. In some cases,

scenarios can go beyond the hand-tuning decision

rule’s description. Conversely, the model-based de-

cision approaches generally discretize the ego car

status into ﬁnite driving statuses and use data-driven

methods to tune the model. In particular, some

papers, such as [12] and [13], propose a uniﬁed

framework to handle decisions and obstacle pre-

diction simultaneously. The consideration of multi-

agent interactions will beneﬁt both prediction and

decision-making processes.

Targeting level-4 autonomous driving, a decision

module shall include both scalability and feasibil-

ity. Scalability is the scenario expression ability

(i.e., the autonomous driving cases that can be

explained). When considering dozens of obstacles,

the decision behavior is difﬁcult to be accurately

described by a ﬁnite set of ego car states. For

feasibility, we mean that the generated decision

shall include a feasible region in which the ego

car can maneuver within dynamic limitations. How-

ever, both hand-tuning and model-based decisions

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