Planning safe trajectories for autonomous vehicles in complex urban
environments is challenging since there are numerous semantic elements (such as
dynamic agents, traffic lights and speed limits) to consider. These semantic
elements may have different mathematical descriptions such as obstacle,
constraint and cost. It is non-trivial to tune the effects from different
combinations of semantic elements for a stable and generalizable behavior. In
this paper, we propose a novel unified spatio-temporal semantic corridor (SSC)
structure, which provides a level of abstraction for different types of
semantic elements. The SSC consists of a series of mutually connected
collision-free cubes with dynamical constraints posed by the semantic elements
in the spatio-temporal domain. The trajectory generation problem then boils
down to a general quadratic programming (QP) formulation. Thanks to the unified
SSC representation, our framework can generalize to any combination of semantic
elements. Moreover, our formulation provides a theoretical guarantee that the
entire trajectory is safe and constraint-satisfied, by using the convex hull
and hodograph properties of piecewise Bezier curve parameterization. We also
release the code of our method to accommodate benchmarking.

本文提出了一种新颖的统一的时空语义走廊结构 (SSC), 为不同类型的语义元素提供了一定的抽象，并解决了自主车辆在复杂城市环境中规划安全轨迹的问题，方法使用广义二次规划 (GQP) 实现轨迹计算，并融合了分段 Bezier 曲线参数化的凸包和豁免特性，提供轨迹安全和合规的理论保障.

利用时空语义走廊在复杂城市环境中生成安全轨迹

Safe Trajectory Generation for Complex Urban Environments Using  Spatio-temporal Semantic Corridor

We study a symmetric collaborative dialogue setting in which two agents, each
with private knowledge, must strategically communicate to achieve a common
goal. The open-ended dialogue state in this setting poses new challenges for
existing dialogue systems. We collected a dataset of 11K human-human dialogues,
which exhibits interesting lexical, semantic, and strategic elements. To model
both structured knowledge and unstructured language, we propose a neural model
with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
Automatic and human evaluations show that our model is both more effective at
achieving the goal and more human-like than baseline neural and rule-based
models.

研究人员提出了一种新型的对话系统，利用动态知识图谱嵌入技术，让两个带有私有知识的代理人进行协作对话，旨在达到某个共同目标，并经过了自动化和人工实验的验证，结果表明该模型与基线模型相比，不仅更有效地实现了目标，而且更加人性化。