This paper introduces UniGen, a novel approach to generating new traffic
scenarios for evaluating and improving autonomous driving software through
simulation. Our approach models all driving scenario elements in a unified
model: the position of new agents, their initial state, and their future motion
trajectories. By predicting the distributions of all these variables from a
shared global scenario embedding, we ensure that the final generated scenario
is fully conditioned on all available context in the existing scene. Our
unified modeling approach, combined with autoregressive agent injection,
conditions the placement and motion trajectory of every new agent on all
existing agents and their trajectories, leading to realistic scenarios with low
collision rates. Our experimental results show that UniGen outperforms prior
state of the art on the Waymo Open Motion Dataset.

UniGen 是一种用于通过模拟生成新交通场景以评估和改进自动驾驶软件的新方法，通过统一建模来模拟所有驾驶场景元素，包括新代理的位置、初始状态和未来运动轨迹，以实现对现有场景中所有上下文的完全条件。该方法结合自回归代理注入，使每个新代理的位置和运动轨迹都依赖于所有已有代理及其轨迹，从而产生具有低碰撞率的逼真场景。在 Waymo Open Motion 数据集上的实验证明 UniGen 优于现有前沿技术。

UniGen：用于生成自动驾驶场景的初始代理状态和轨迹的统一建模

UniGen: Unified Modeling of Initial Agent States and Trajectories for  Generating Autonomous Driving Scenarios

Simulation-based testing represents an important step to ensure the
reliability of autonomous driving software. In practice, when companies rely on
third-party general-purpose simulators, either for in-house or outsourced
testing, the generalizability of testing results to real autonomous vehicles is
at stake.
In this paper, we strengthen simulation-based testing by introducing the
notion of digital siblings, a novel framework in which the AV is tested on
multiple general-purpose simulators, built with different technologies. First,
test cases are automatically generated for each individual simulator. Then,
tests are migrated between simulators, using feature maps to characterize of
the exercised driving conditions. Finally, the joint predicted failure
probability is computed and a failure is reported only in cases of agreement
among the siblings.
We implemented our framework using two open-source simulators and we
empirically compared it against a digital twin of a physical scaled autonomous
vehicle on a large set of test cases. Our study shows that the ensemble failure
predictor by the digital siblings is superior to each individual simulator at
predicting the failures of the digital twin. We discuss several ways in which
our framework can help researchers interested in automated testing of
autonomous driving software.

通过引入数字同胞的概念，本研究提出一种新的模拟测试框架，该框架可以在多个通用模拟器上测试自动驾驶软件，并可以计算集成预测失败概率，并将失败仅在同胞之间达成一致时报告。研究结果表明，数字同胞的集成失败预测器优于每个单独的模拟器，可以帮助感兴趣的研究人员进行自动化测试的研究。