Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in a one-shot or step-wise manner. Although one-shot approaches are usually preferred for their simplicity, they relinquish powerful self-supervision schemes that can be constructed by chaining multiple time-steps. We address this issue by proposing a middle-ground where multiple trajectory segments are chained together. Our proposed Multi-Branch Self-Supervised Predictor receives additional training on new predictions starting at intermediate future segments. In addition, the model 'imagines' the latent context and 'predicts the past' while combining multi-modal trajectories in a tree-like manner. We deliberately keep aspects such as interaction and environment modeling simplistic and nevertheless achieve competitive results on the INTERACTION dataset. Furthermore, we investigate the sparsely explored uncertainty estimation of deterministic predictors. We find positive correlations between the prediction error and two proposed metrics, which might pave way for determining prediction confidence.

翻译：精确的车辆轨迹预测是自动驾驶中一个尚未解决的问题，包含诸多开放研究课题。现有最优方法通常以单步（one-shot）或逐步（step-wise）方式回归轨迹。尽管单步方法因其简洁性常受青睐，但其放弃了通过串联多个时间步构建的强大自监督机制。我们通过提出一种中间方案来解决这一问题：将多个轨迹片段链式拼接。我们提出的多分支自监督预测器（Multi-Branch Self-Supervised Predictor）在从中间未来片段开始的预测中额外接收训练。此外，该模型以树状方式组合多模态轨迹时，能“想象”潜在上下文并“预测过去”。我们刻意保持交互与环境建模等模块的简洁性，却在INTERACTION数据集上取得了具有竞争力的结果。此外，我们探索了确定性预测器稀疏研究的不确定性估计问题。研究发现预测误差与两个提议指标呈正相关，这可能为确定预测置信度开辟新路径。