Safety is one of the major concerns in autonomous driving tasks, and enhancing the
collision avoidance ability of connected and autonomous vehicles (CAVs) is an effective way …

Reactive and safe agent modellings are important for nowadays traffic simulator designs
and safe planning applications. In this work, we proposed a reactive agent model which can …

We present an autonomous driving planning algorithm that takes into account neighboring
drivers' behaviors and achieves safer and more efficient navigation. Our approach leverages …

In this paper, we present a Model Predictive Control (MPC) framework based on path
velocity decomposition paradigm for autonomous driving. The optimization underlying the …

Connected and automated vehicles (CAVs) extend urban traffic control from temporal to
spatiotemporal by enabling the control of CAV trajectories. Most of the existing studies on …

Hybrid traffic which involves both autonomous and human-driven vehicles would be the
norm of the autonomous vehicles' practice for a while. On the one hand, unlike autonomous …

The sensing capabilities of smart vehicles are growing and enabling unprecedented
functionalities that drive the future commercial adoption of autonomous vehicles. However …

Within a robot autonomy stack, the planner and controller are typically designed separately,
and serve different purposes. As such, there is often a diffusion of responsibilities when it …

eCloud: A Vision for the Evolution of the Edge-Cloud Continuum Page 1 COVER FEATURE
COMPUTING FOR AUTONOMY: LATENCY, POWER, RESILIENCE 24 COMPUTER …

We present a novel algorithm for computing collision-free navigation for heterogeneous road-
agents such as cars, tricycles, bicycles, and pedestrians in dense traffic. Our approach …