Analysis of Efficient Transmission Methods of Grid Maps for Intelligent Vehicles

Robin Dehler; Dominik Authaler; Aryan Thakur; Thomas Wodtko; Michael Buchholz

cs.ROApr 2, 2026

Analysis of Efficient Transmission Methods of Grid Maps for Intelligent Vehicles

Authors: Robin Dehler, Dominik Authaler, Aryan Thakur, Thomas Wodtko, Michael Buchholz

Organizations: Institute of Measurement, Control and Microtechnology, Ulm University, Albert-Einstein-Allee 41, 89081 Ulm, Germany

Abstract

Grid mapping is a fundamental approach to modeling the environment of intelligent vehicles or robots. Compared with object-based environment modeling, grid maps offer the distinct advantage of representing the environment without requiring any assumptions about objects, such as type or shape. For grid-map-based approaches, the environment is divided into cells, each containing information about its respective area, such as occupancy. This representation of the entire environment is crucial for achieving higher levels of autonomy. However, it has the drawback that modeling the scene at the cell level results in inherently large data sizes. Patched grid maps tackle this issue to a certain extent by adapting cell sizes in specific areas. Nevertheless, the data sizes of patched grid maps are still too large for novel distributed processing setups or vehicle-to-everything (V2X) applications. Our work builds on a patch-based grid-map approach and investigates the size problem from a communication perspective. To address this, we propose a patch-based communication pipeline that leverages existing compression algorithms to transmit grid-map data efficiently. We provide a comprehensive analysis of this pipeline for both intra-vehicle and V2X-based communication. The analysis is verified for these use cases with two real-world experiment setups. Finally, we summarize recommended guidelines for the efficient transmission of grid-map data in intelligent transportation systems.

Figures & tables

Fig. 1: V2X use case example for the presented approach. The analyzed compression step, including decompression, enables V2X applications for local grid maps (green and yellow). Then, the grid fusion of multiple grid maps from multiple CAVs can be done on the server. The fused grid (red) can then be returned to the CAVs for various applications. The wireless transmission is indicated with the dashed arrows.

Panel of Figure 2 — Fig. 2: Simple overview of patch-wise compression. Each patch, indicated with different colors, is compressed separately before the whole ROS 2 message is serialized. For reconstruction, the whole ROS 2 message is deserialized, and each patch is again decompressed separately.

Panel of Figure 3 — Fig. 3: Comparison of the summed time of (i) serialization, (ii) compression, (iii) decompression, and (iv) deserialization of the whole ROS 2 message to the patch-wise compression pipeline with (i) patch-wise compression, (ii) serialization, (iii) deserialization, and (iv) patch-wise decompression. Patch-wise compression is indicated with the subscript $P$ .

Panel of Figure 4 — Fig. 4: Time and compressed size comparison of different acceleration parameters for LZ4 compression for both normal (left) and quantized (right) data.

Panel of Figure 5 — Fig. 5: Time and compressed size comparison of different compression level parameters for Zstd compression for normal (left) and quantized (right) data.

Bandwidth	10 Mbps	100 Mbps	1 Gbps	10 Gbps
LZ4	$1;179.2$	$1;18.9$	$1;2.84$	$64;1.13$
Zstd	$8;120.1$	$2;13.8$	$-1;2.93$	$-62;1.36$
LZ4 q	$1;82.3$	$1;8.64$	$1;1.27$	$128;0.47$
Zstd q	$7;48.8$	$1;5.81$	$-1;1.34$	$-60;0.56$

TABLE I: Optimal parameter choice with end-to-end times for each considered bandwidth:

a_{\text{LZ4}}/c_{\text{Zstd}};t_{\text{e2e}}[ms]

Panel of Figure 7 — Fig. 6: Comparison of different end-to-end times including its components given different bandwidths for both normal and quantized data. The chosen optimal parameter for the plots are equivalent with Table I . The legend on the top applies to all 4 plots (a)-(d).

Use-Case		None	LZ4	Zstd	None q	LZ4 q	Zstd q
V2X	mean	$3623.7$	$567.8$	$304.8$	$1450.2$	$239.6$	$201.5$
	std	$3353.1$	$470.7$	$152.8$	$1388.0$	$85.5$	$129.03$
	min	$1055.3$	$212.6$	$123.3$	$301.7$	$147.1$	$66.8$
	max	$15159$	$2160.6$	$830.5$	$7394.5$	$493.8$	$629.9$
DPU	mean	$6.43$	$5.16$	$6.29$	$2.11$	$2.11$	$2.50$
	std	$2.55$	$1.14$	$1.36$	$0.36$	$0.44$	$0.74$

TABLE II: Real-world evaluation for each compression method with different time values in

\left[

\text{,}\mathrm{ms}

\right]

Intelligent Transportation Systems

Intelligent Transportation Systems apply sensing, communication, AI, and optimization to make transport networks safer and more efficient.

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Occupancy Grid Mapping Simultaneous Localization and Mapping

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Determining the occupancy status of locations in the environment is a fundamental task for safety-critical robotic applications. Traditional occupancy grid mapping methods subdivide the environment into a grid of voxels, each associated with one of three occupancy states: free, occupied, or unknown. These methods explicitly maintain all voxels within the mapped volume and determine the occupancy state of a location by directly querying the corresponding voxel that the location falls within. However, maintaining all grid voxels in high-resolution and large-scale scenarios requires substantial memory resources. In this paper, we introduce a novel representation that only maintains the boundary of the mapped volume. Specifically, we explicitly represent the boundary voxels, such as the occupied voxels and frontier voxels, while free and unknown voxels are automatically represented by volumes within or outside the boundary, respectively. As our representation maintains only a closed surface in two-dimensional (2D) space, instead of the entire volume in three-dimensional (3D) space, it significantly reduces memory consumption. Then, based on this 2D representation, we propose a method to determine the occupancy state of arbitrary locations in the 3D environment. We term this method as boundary map. Besides, we design a novel data structure for maintaining the boundary map, supporting efficient occupancy state queries. Theoretical analyses of the occupancy state query algorithm are also provided. Furthermore, to enable efficient construction and updates of the boundary map from the real-time sensor measurements, we propose a global-local mapping framework and corresponding update algorithms. Finally, we will make our implementation of the boundary map open-source on GitHub to benefit the community:https://github.com/hku-mars/BDM.

Abstract

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