From the 1 of 6 linked papers with an AI index.
6 papers
Risk-Aware Motion Planning with Learned Trajectory Primitives and Probabilistic Safety Assessment
Marc Kaufeld, Dian Zhuang, Johannes Betz
The paper proposes a motion planning framework for urban autonomous driving that uses a radial basis function network to generate jerk‑minimal trajectory primitives and evaluates t…
G2DP: Diffusion Planning with Spatio-Temporal Grid Guidance
Hang Yu, Ye Jin, Alessandro Canevaro +7
In autonomous driving, diffusion-based planners have emerged as a promising paradigm for robust motion planning in dense and interactive traffic, as they can effectively model dive…
Precise and Efficient Collision Prediction under Uncertainty in Autonomous Driving
Marc Kaufeld, Johannes Betz
This research introduces two efficient methods to estimate the collision risk of planned trajectories in autonomous driving under uncertain driving conditions. Deterministic collis…
MP-RBFN: Learning-based Vehicle Motion Primitives using Radial Basis Function Networks
Marc Kaufeld, Mattia Piccinini, Johannes Betz
This research introduces MP-RBFN, a novel formulation leveraging Radial Basis Function Networks for efficiently learning Motion Primitives derived from optimal control problems for…
MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validation
Marc Kaufeld, Korbinian Moller, Alessio Gambi +2
Scenario-based testing using simulations is a cornerstone of Autonomous Vehicles (AVs) software validation. So far, developers needed to choose between low-fidelity 2D simulators t…
DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving
Dingrui Wang, Marc Kaufeld, Johannes Betz
We present a novel autonomous driving framework, DualAD, designed to imitate human reasoning during driving. DualAD comprises two layers: a rule-based motion planner at the bottom…