Baidu Apollo实时自动驾驶路径规划系统

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"Apollo规划论文介绍了基于百度Apollo开源自动驾驶平台的实时运动规划系统，该系统旨在解决工业级四级（Level-4）自动驾驶的安全、舒适和可扩展性问题。它包括多车道和单车道驾驶的分层策略，以及路径和速度优化算法。" 在本文中，作者详细阐述了BaiduApolloEMMotionPlanner的设计与实现，这是一个针对高速公路和低速城市驾驶场景都具有可扩展性的实时规划解决方案。系统的核心策略分为两个层次： 1. 多车道策略：系统的主要参与者是一个多车道策略模块，它通过并行计算不同车道的轨迹来处理车道变换情况。这一策略能够智能地比较各个车道的行驶路径，以选择最安全、最舒适的行驶路线。 2. 车道内轨迹生成器：在单个车道内，系统采用迭代方法解决路径和速度优化问题。这基于Frenet坐标系进行，这是一种在道路几何结构中表示车辆运动的有效方式。在路径和速度优化方面，文章提出了一种结合动态规划和样条基二次规划的方法。这种方法构建了一个可扩展且易于调整的框架，能够同时处理交通规则、障碍物决策和行驶平顺性。动态编程用于处理离散决策，如交通标志和交叉路口规则；而样条基二次规划则用于连续空间的平滑路径生成，确保车辆运动的流畅性。此外，该规划器的可扩展性是其一大亮点，能够适应高速公路上的高速行驶以及城市环境中的低速驾驶。通过实际场景图解和路测结果，作者展示了算法的有效性和适用性。这不仅证明了系统的性能，也为自动驾驶技术的工业应用提供了有力的支持。总结来说，"Apollo规划论文"提供了一种综合的实时运动规划方法，通过多层次的决策和优化，实现了在复杂交通环境中的安全、高效自动驾驶。其创新之处在于将路径规划和速度优化相结合，并且具有高度的可扩展性，适应不同的驾驶场景。这种技术对于推动自动驾驶行业的进步具有重要意义。

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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