IROS

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\title{Lattice-Adaptive Motion Planning for\\High-Speed Off-Road Driving}

\author{\IEEEauthorblockN{First Last}
\IEEEauthorblockA{\textit{University of Example}\\[email protected]}
\and
\IEEEauthorblockN{Jane Doe}
\IEEEauthorblockA{\textit{Example Research Labs}\\[email protected]}}
\maketitle

\begin{abstract}
High-speed off-road driving stresses planners with rapidly shifting
feasibility boundaries. We propose a lattice-adaptive planner that
dynamically refines the motion primitive set based on local terrain
complexity. Field tests over 40 hours show a 28\% reduction in planning
failures and 16\% increase in average speed versus a fixed-lattice
baseline.
\end{abstract}

\begin{IEEEkeywords}
motion planning, off-road, autonomous driving
\end{IEEEkeywords}

\section{Introduction}
Off-road terrain demands planners that react to both large-scale
obstacles and fine-grained traction variations.

\section{Related Work}
State-lattice planners, RRT* for off-road, kinodynamic planning.

\section{Method}
We maintain a coarse base lattice and refine primitives where a terrain
score detects high complexity:
\begin{equation}
  \mathcal{R}(x) = \beta_1 \rho(x) + \beta_2 \sigma_{\text{slope}}(x) + \beta_3 |\kappa(x)|.
\end{equation}

\begin{theorem}
The lattice-adaptive planner is probabilistically complete if the base
lattice is.
\end{theorem}

\section{Experiments}
\begin{table}[t]
\centering\small
\begin{tabular}{lcc}
\toprule
Terrain & Fixed lattice (failures) & \textbf{Ours} \\
\midrule
Rocky trail   & 11 & \textbf{3} \\
Mud           &  8 & \textbf{2} \\
Dune          & 14 & \textbf{6} \\
Rutted track  &  9 & \textbf{4} \\
\bottomrule
\end{tabular}
\caption{Failures per 10 hours of autonomous driving.}
\end{table}

\section{Conclusion}
Adapting motion primitives to terrain complexity is a simple but
effective lever for off-road autonomy.

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