百度Apollo EM运动规划器：工业级自动驾驶解决方案

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Baidu Apollo EM Motion Planner 是百度开源的自动驾驶平台中的一项关键组件，专注于工业级自动驾驶（Level 4）的实时路径规划。该论文发表于2018年7月，由包括Haoyang Fan、Fan Zhu等在内的10位作者共同完成，他们在JD.com USA的Qi Kong团队中也参与了相关的项目开发。论文的核心内容聚焦于设计一个兼顾安全、舒适性和可扩展性的实时运动规划系统。它采用了多层（hierarchical）的策略来解决复杂的自动驾驶场景。首先，系统采用一个多车道策略，负责处理车道变换，确保在多车道行驶中的顺畅和高效。这涉及到对周围交通状况的精确感知、预测以及决策制定，以适应动态的道路环境。在单车道驾驶方面，系统同样注重细节，通过精细的路径规划算法，确保车辆能够在狭窄空间内稳定、流畅地行驶，同时避免碰撞潜在障碍物。为了提升驾驶体验，舒适性因素也被纳入规划过程，如减小加速度和转向角的变化，减少乘客的不适感。此外，Baidu Apollo EM Motion Planner 还考虑了系统的可扩展性，这意味着它能够适应不同规模的城市道路和复杂的交通条件，具备良好的适应性和灵活性。开发者们通过不断的优化和迭代，使系统能够应对不断变化的现实挑战。这篇预印本的作者们不仅展示了他们的技术实力，还强调了与实际项目AutonomousDrivingView的关联，表明研究成果已应用于实际的自动驾驶开发中。截至2019年12月30日，该论文已被引用471次，显示其在行业内有一定的影响力。 Baidu Apollo EM Motion Planner 是一个重要的技术贡献，它展示了百度在自动驾驶领域对于复杂场景下路径规划的深入理解和实践经验，为其他开发者和研究者提供了有价值的参考。随着自动驾驶技术的发展，这个系统将持续改进和升级，推动行业的进步。

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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百度Apollo EM运动规划器：工业级自动驾驶解决方案

Baidu Apollo EM Motion Planner_路径规划_motionplanner_自动驾驶决策_自动驾驶_ap

Baidu Apollo EM Motion Planner.zip

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Fan 等。 - 2018 - Baidu Apollo EM Motion Planner1

Fan 等。 - 2018 - Baidu Apollo EM Motion Planner3

考古apollo3.0 EMplanner笔记rawio

EMplanner注释版本matlab代码，可刀 该算法使用dp动态规划进行了轨迹规划，过程中未向apollo的EM规划器一样还

百度Apollo的lattice planner算法idea来源的参考论文

百度Apollo自动驾驶EM规划算法详解

Apollo EM运动规划器在自动驾驶路径规划中的应用

最新资源

EMplanner注释版本matlab代码，可刀该算法使用dp动态规划进行了轨迹规划，过程中未向apollo的EM规划器一样还