ing plans as a combination of discrete driving behaviors and
velocity estimations. For instance, the combination of a driv-
ing behavior change lane to left and a velocity esti-
mation deceleration results in a high-level driving plan
change lane to left with deceleration. We
instruct an LLM via in-context learning to devise a high-level
driving plan based on environmental information, memory
data, and chain-of-thought reasoning results. The devised
high-level driving plan characterizes the ego-vehicle’s coarse
locomotion and serves as a strong prior to guide the
subsequent motion planning process.
Motion Planning. Motion planning aims to devise a safe
and comfortable trajectory for driving, and each trajectory is
represented as a sequence of waypoints. Following [26], we
re-formulate motion planning as a language modeling prob-
lem. Specifically, we leverage environmental information,
memory data, reasoning results, and high-level driving plans
ALanguageAgentforAutonomousDriving