The lane graph is critical for applications such as autonomous driving and lane-level route planning. While previous research has focused on extracting lane-level graphs from aerial imagery using convolutional neural networks (CNNs) followed by post-...
This paper presents a robust, efficient lane-marking feature extraction method using a graph model-based approach. To extract the features, the proposed hat filter with adaptive sizes is first applied to each row of an input image and local maximum v...
It is desirable to maintain high accuracy and runtime efficiency at the same time in lane detection. However, due to the long and thin properties of lanes, extracting features with both strong discrimination and perception abilities needs a huge amou...
This paper presents a matrix-based approach for motion planning of autonomous vehicles in structured parking environments under Temporal Logic (TL) constraints. Instead of relying solely on traditional automaton models, we construct a product automat...
This paper presents the Cell Transformer (CeT), which utilizes high-definition (HD) map data to predict future traffic states at signalized intersections, thereby aiding trajectory planning for autonomous vehicles. CeT employs discretized lane segmen...
This study proposes a design method for the evaluation and redesign of product–service systems (PSSs) from the perspective of social sustainability, one that applies Max-Neef’s framework of fundamental human needs. The proposed method sys...
Most of the existing research in the field of autonomous vehicles (AVs) addresses decision making, planning and control as separate factors which may affect AV performance in complex driving environments. A hierarchical framework is proposed in this...
With the rapid evolution of intelligent driving technology, vehicle trajectory prediction has become a pivotal technique for enhancing road safety and traffic efficiency. In this domain, high-definition vector maps and graph neural networks (GNNs) pl...