Trajectory prediction, as a critical component of autonomous driving systems, has attracted the attention of many researchers. Existing prediction algorithms focus on extracting more detailed scene features or selecting more reasonable trajectory destinations. However, in the face of dynamic and evolving future movements of the target vehicle, these algorithms cannot provide a fine-grained and continuous description of future behaviors and lane constraints, which degrades the prediction accuracy. To address this challenge, we present BLNet, a novel dualstream architecture that synergistically integrates behavioral intention recognition and lane constraint modeling through parallel attention mechanisms. The framework generates fine-grained behavior state queries (capturing spatial-temporal movement patterns) and lane queries (encoding lane topology constraints), supervised by two auxiliary losses, respectively. Subsequently, a two-stage decoder first produces trajectory proposals, then performs point-level refinement by jointly incorporating both the continuity of passed lanes and future motion features. Extensive experiments on two large datasets, nuScenes and Argoverse, show that our network exhibits significant performance gains over existing direct regression and goal-based algorithms.

翻译：轨迹预测作为自动驾驶系统的关键组成部分，已引起众多研究者的关注。现有预测算法侧重于提取更精细的场景特征或选择更合理的轨迹终点。然而，面对目标车辆动态演化的未来运动，这些算法无法对未来行为与车道约束提供细粒度且连续的描述，从而降低了预测精度。为应对这一挑战，我们提出BLNet——一种新颖的双流架构，通过并行注意力机制协同整合行为意图识别与车道约束建模。该框架分别生成细粒度行为状态查询（捕获时空运动模式）与车道查询（编码车道拓扑约束），并分别通过两个辅助损失进行监督。随后，一个两阶段解码器首先生成轨迹提案，进而通过联合融入已通过车道的连续性与未来运动特征进行点级轨迹优化。在nuScenes和Argoverse两大数据集上的大量实验表明，我们的网络相较于现有直接回归算法与基于目标点的算法均展现出显著的性能提升。