  LiDAR sensors play a crucial role in various applications, especially in
autonomous driving. Current research primarily focuses on optimizing perceptual
models with point cloud data as input, while the exploration of deeper
cognitive intelligence remains relatively limited. To address this challenge,
parallel LiDARs have emerged as a novel theoretical framework for the
next-generation intelligent LiDAR systems, which tightly integrate physical,
digital, and social systems. To endow LiDAR systems with cognitive
capabilities, we introduce the 3D visual grounding task into parallel LiDARs
and present a novel human-computer interaction paradigm for LiDAR systems. We
propose Talk2LiDAR, a large-scale benchmark dataset tailored for 3D visual
grounding in autonomous driving. Additionally, we present a two-stage baseline
approach and an efficient one-stage method named BEVGrounding, which
significantly improves grounding accuracy by fusing coarse-grained sentence and
fine-grained word embeddings with visual features. Our experiments on
Talk2Car-3D and Talk2LiDAR datasets demonstrate the superior performance of
BEVGrounding, laying a foundation for further research in this domain.
