In this paper, we introduce a method for estimating blind spots for sensor
setups of autonomous or automated vehicles and/or robotics applications. In
comparison to previous methods that rely on geometric approximations, our
presented approach provides more realistic coverage estimates by utilizing
accurate and detailed 3D simulation environments. Our method leverages point
clouds from LiDAR sensors or camera depth images from high-fidelity simulations
of target scenarios to provide accurate and actionable visibility estimates. A
Monte Carlo-based reference sensor simulation enables us to accurately estimate
blind spot size as a metric of coverage, as well as detection probabilities of
objects at arbitrary positions.

本论文介绍了一种用于估计自动驾驶车辆和 / 或机器人应用的传感器设置的盲区的方法。与依赖几何近似的先前方法相比，我们提出的方法利用准确且详细的 3D 模拟环境，提供了更实际的覆盖范围估计。我们的方法利用来自 LiDAR 传感器的点云或高保真仿真目标场景的相机深度图像，提供准确且可操作的可见性估计。基于蒙特卡洛的参考传感器仿真使我们能够准确地估计盲区大小作为覆盖度度量，并了解任意位置上的物体检测概率。

自动驾驶车辆的盲点估计基于基于场景的随机参考传感器模拟

Can you see me now? Blind spot estimation for autonomous vehicles using  scenario-based simulation with random reference sensors

Given a pre-trained CNN without any testing samples, this paper proposes a
simple yet effective method to diagnose feature representations of the CNN. We
aim to discover representation flaws caused by potential dataset bias. More
specifically, when the CNN is trained to estimate image attributes, we mine
latent relationships between representations of different attributes inside the
CNN. Then, we compare the mined attribute relationships with ground-truth
attribute relationships to discover the CNN's blind spots and failure modes due
to dataset bias. In fact, representation flaws caused by dataset bias cannot be
examined by conventional evaluation strategies based on testing images, because
testing images may also have a similar bias. Experiments have demonstrated the
effectiveness of our method.

本研究提出了一种简单而有效的方法来诊断 CNN 的特征表示，以发现由数据库偏差引起的表示缺陷和失败模式，并使用隐含属性关系和基础真相关系进行比较来挖掘潜在关联性。实验表明该方法的有效性。

针对数据集偏差的 CNN 表示研究

Examining CNN Representations with respect to Dataset Bias

Video prediction models based on convolutional networks, recurrent networks,
and their combinations often result in blurry predictions. We identify an
important contributing factor for imprecise predictions that has not been
studied adequately in the literature: blind spots, i.e., lack of access to all
relevant past information for accurately predicting the future. To address this
issue, we introduce a fully context-aware architecture that captures the entire
available past context for each pixel using Parallel Multi-Dimensional LSTM
units and aggregates it using blending units. Our model outperforms a strong
baseline network of 20 recurrent convolutional layers and yields
state-of-the-art performance for next step prediction on three challenging
real-world video datasets: Human 3.6M, Caltech Pedestrian, and UCF-101.
Moreover, it does so with fewer parameters than several recently proposed
models, and does not rely on deep convolutional networks, multi-scale
architectures, separation of background and foreground modeling, motion flow
learning, or adversarial training. These results highlight that full awareness
of past context is of crucial importance for video prediction.

通过使用并行多维 LSTM 单元和融合单元进行数据聚合处理，提出了一种全面考虑过去背景信息的上下文感知结构，从而提高了视频预测的性能。该模型没有使用深度卷积网络、多尺度结构、分离前景和背景建模、运动流学习或对抗性训练等技术，性能超过了强基线模型并在多个领域展示了最新成果。