One way to approach end-to-end autonomous driving is to learn a policy function that maps
from a sensory input, such as an image frame from a front-facing camera, to a driving action …

Vision-based autonomous urban driving in dense traffic is quite challenging due to the
complicated urban environment and the dynamics of the driving behaviors. Widely-applied …

We present an end-to-end imitation learning system for agile, off-road autonomous driving
using only low-cost on-board sensors. By imitating a model predictive controller equipped …

Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …

The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive
open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the …

Reinforcement learning is considered as a promising direction for driving policy learning.
However, training autonomous driving vehicle with reinforcement learning in real …

Simulation can be a powerful tool for under-standing machine learning systems and
designing methods to solve real-world problems. Training and evaluating methods purely in …

End-to-end approaches to autonomous driving commonly rely on expert demonstrations.
Although humans are good drivers, they are not good coaches for end-to-end algorithms …

Data-driven simulation has become a favorable way to train and test autonomous driving
algorithms. The idea of replacing the actual environment with a learned simulator has also …

In this paper, we explore the potential of using a large language model (LLM) to understand
the driving environment in a human-like manner and analyze its ability to reason, interpret …