Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. But the impact of those methods in learning diverse hypotheses is under-studied as such objectives highly depend on their initialization for diversity. As our first contribution, we propose a novel Divide-And-Conquer (DAC) approach that acts as a better initialization technique to WTA objective, resulting in diverse outputs without any spurious modes. Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes. Our framework provides multi-agent trajectory outputs in a forward pass by capturing interactions through hypercolumn descriptors and incorporating scene information in the form of rasterized images and per-agent lane anchors. Experiments on synthetic and real data show that the proposed DAC captures the data distribution better compare to other WTA family of objectives. Further, we show that our ALAN approach provides on par or better performance with SOTA methods evaluated on Nuscenes urban driving benchmark.
翻译:我们的工作解决了轨迹预测、学习多式联运产出和通过使用驾驶知识施加限制进行更好的预测方面的两大挑战。最近的方法利用“全赢(WTA)”或“多赢”等多球学习目标取得了强劲的成绩。但是,这些方法在学习各种假设方面的影响没有得到充分研究,因为这些目标在很大程度上取决于对多样性的初始化。作为我们的第一个贡献,我们提出了一种新的分化(DAC)方法,作为对WTA目标的更好初始化技术,导致没有任何虚假模式的多种产出。我们的第二个贡献是称为“ALAN”的新的轨迹预测框架,它利用现有的航道中线作为锚,提供受投入航道限制的轨迹。我们的框架提供了多试剂轨迹产出,因为通过超柱式描述器捕捉互动,将场景信息以压化图像和每位代理车道锚的形式纳入。关于合成和真实数据的实验进一步实验表明,拟议的ASLA将更好的业绩分配方法与我们所评估的SOLTA目标相比较。