Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

In the area of autonomous driving, it typically brings great difficulty in solving the motion
planning problem since the vehicle model is nonlinear and the driving scenarios are …

Cooperative trajectory planning of connected autonomous vehicles (CAVs) generally admits
strong nonlinearity and non-convexity, rendering great difficulties in finding the optimal …

We propose a new learning framework, signal propagation (sigprop), for propagating a
learning signal and updating neural network parameters via a forward pass, as an …

This work investigates a game-theoretic optimization approach towards robust control of
uncertain dynamical systems with state and input constraints. The uncertainty involved is …

The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs) has
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …

In dense traffic scenarios, ensuring safety while keeping high task performance for
autonomous driving is a critical challenge. To address this problem, this paper proposes a …

This paper proposes a new reinforcement learning (RL) algorithm, called encoding
distributional soft actor-critic (E-DSAC), for decision-making in autonomous driving. Unlike …

In this manuscript, new combination methodology is proposed, which named combining
Passivity-Based Control and Linear Quadratic Regulator (for short, CPBC-LQR), to support …

The Hamilton–Jacobi–Bellman (HJB) equation serves as the necessary and sufficient
condition for the optimal solution to the continuous-time (CT) optimal control problem (OCP) …