Baidu Apollo自动驾驶平台实时运动规划系统

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"本文介绍了基于Baidu Apollo开源自动驾驶平台的实时运动规划系统，旨在解决工业级四级自动驾驶中的安全、舒适和可扩展性问题。系统通过多层次的方式处理多车道和单车道驾驶：1）系统的主要策略是多车道策略，通过并行计算车道级轨迹来处理变道场景；2）在车道级轨迹生成器内，它迭代地解决基于Frenet框架的路径和速度优化问题；3）为了路径和速度优化，提出了动态规划和样条基二次规划的组合，构建了一个可扩展且易于调整的框架，以同时处理交通规则、障碍物决策和平滑度。该规划器可扩展到高速公路和低速城市驾驶场景。" 文章详细介绍了百度Apollo EM运动规划器的设计与实现，这是一款针对自动驾驶的实时决策系统。首先，该系统采用了层次化的方法来处理自动驾驶中的复杂路况。在高层次上，多车道策略扮演了核心角色，其主要任务是处理车辆的变道行为。通过并行计算不同车道的行驶轨迹，系统可以在短时间内对比分析，选择最安全、最舒适的行驶路线。在低层次，即车道级轨迹生成阶段，系统采用迭代的方式进行路径和速度的优化。这涉及到Frenet框架的应用，这是一个在曲线坐标系中描述车辆运动的数学工具，能够更直观地处理车辆在车道内的位置和方向变化。通过在Frenet框架下解决路径和速度优化问题，可以更好地考虑到实际驾驶中的动态因素。路径和速度优化算法是规划器的关键组成部分。结合动态规划和样条基的二次规划，系统能够在满足交通规则的同时，处理障碍物的规避决策，并确保驾驶过程的平滑性。动态规划用于全局规划，考虑长远的最优决策，而样条基二次规划则提供了局部优化，确保路径的连续性和舒适性。此外，该系统具有高度的可扩展性，不仅能够适应高速公路的高速驾驶环境，也能应对城市道路的低速驾驶情况。这意味着不论是在开阔的高速公路还是复杂的市区街道，百度Apollo EM运动规划器都能够有效地为自动驾驶车辆提供导航和决策支持。这个运动规划系统通过创新的算法和技术，实现了自动驾驶中的安全、舒适和高效行驶，为实际应用中的四级自动驾驶提供了强大的理论和技术支撑。

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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