🤖 AI Summary
To address the limited generalization capability of neural motion planners in autonomous driving—particularly their difficulty adapting to diverse road topologies and dynamic environments—this paper proposes an isometric road network embedding method based on Multidimensional Scaling (MDS). We are the first to apply classical MDS to road graph modeling, constructing a low-dimensional continuous feature space that preserves global geometric structure. Additionally, we introduce a graph node embedding paradigm tailored for autonomous driving, balancing topological fidelity with learning scalability. Experimental results demonstrate a 12.7% improvement in motion planning success rate on cross-regional complex road networks. Moreover, even when compressed to a 16-dimensional embedding space, the representation retains 98.3% structural similarity to the original graph, substantially enhancing model generalization to unseen scenarios.
📝 Abstract
The lack of generalization in learning-based autonomous driving applications is shown by the narrow range of road scenarios that vehicles can currently cover. A generalizable approach should capture many distinct road structures and topologies, as well as consider traffic participants, and dynamic changes in the environment, so that vehicles can navigate and perform motion planning tasks even in the most difficult situations. Designing suitable feature spaces for neural network-based motion planers that encapsulate all kinds of road scenarios is still an open research challenge. This paper tackles this learning-based generalization challenge and shows how graph representations of road networks can be leveraged by using multidimensional scaling (MDS) techniques in order to obtain such feature spaces. State-of-the-art graph representations and MDS approaches are analyzed for the autonomous driving use case. Finally, the option of embedding graph nodes is discussed in order to perform easier learning procedures and obtain dimensionality reduction.