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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