This paper introduces a digital-twin (DT)-assisted cooperative perception framework designed to improve perception accuracy under end-to-end (E2E) latency constraints and to balance perception accuracy and E2E latency under communication resource constraints in autonomous vehicles. We formulate an optimization problem that maximizes perception accuracy subject to latency and communication limitations, and solve it using a newly proposed coarse-to-fine search (CTFS) algorithm. Simulation results show that the proposed CTFS algorithm achieves 96.6% perception accuracy, close to exhaustive search, under latency constraints while reducing computational complexity by approximately 85.78%. The DT-assisted framework further achieves a 50% reduction in the non-DT communication cost through estimated, time-synchronized state updates.
Digital twins are used across many industries to enable better decision making. However, while policy makers at all levels (including city, national and supranational scales) have expressed a desire to integrate digital twins into their workflows, this adoption has been slow to m…
In autonomous driving development, a perception dataset is crucial, as it provides fundamental data for training, testing, and validating algorithms for an autonomous vehicle's multimodal perception systems. So far, most research has concentrated on providing datasets for well-st…
Autonomously controlling and handling a vehicle at and beyond its stability limit is a mathematically and computationally demanding task. Prior demonstrations of automated drifting have been limited to research platforms with instantaneous torque delivery and independently actuat…
In this letter, we present a strategy for autonomous docking of autonomous vehicles in three-dimensional space. Docking is a safety-critical task and requires expert piloting skills. Vehicles with autonomous docking capabilities are highly desirable in various applications, such…
Model Predictive Control (MPC) has been widely applied to autonomous vehicle drifting. Assisted drifting, that is where the driver remains in the loop, is still comparatively underexplored. Existing approaches often rely on restrictive assumptions, such as precomputed drift equil…
Understanding human responses to autonomous vehicle (AV) behaviors is essential for socially aware interaction, which is crucial for socially compatible navigation in shared traffic environments. We characterize human driving responses in interactions with AVs as feedback laws ov…