Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving
experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The …

We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model
into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge …

In this paper, we propose a neural motion planner for learning to drive autonomously in
complex urban scenarios that include traffic-light handling, yielding, and interactions with …

A growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their
promise for enhanced safety, efficiency, and economic benefits. While previous surveys …

Principles of typical motion planning algorithms are investigated and analyzed in this paper.
These algorithms include traditional planning algorithms, classical machine learning …

Curvy roads are a particular type of urban road scenario, wherein the curvature of the road
centerline changes drastically. This paper is focused on the trajectory planning task for …

As driving scenarios and autonomous vehicles (AVs) become increasingly intricating, there
is an increasing need for innovative frameworks that can enhance and test AV capabilities …

Today's self-driving vehicles have achieved impressive driving capabilities, but still suffer
from uncertain performance in long-tail cases. Training a reinforcement-learning-based self …

Human-level driving is an ultimate goal of autonomous driving. Conventional approaches
formulate autonomous driving as a perception-prediction-planning framework, yet their …