Object detection plays a critical role in autonomous driving, where
accurately and efficiently detecting objects in fast-moving scenes is crucial.
Traditional frame-based cameras face challenges in balancing latency and
bandwidth, necessitating the need for innovative solutions. Event cameras have
emerged as promising sensors for autonomous driving due to their low latency,
high dynamic range, and low power consumption. However, effectively utilizing
the asynchronous and sparse event data presents challenges, particularly in
maintaining low latency and lightweight architectures for object detection.
This paper provides an overview of object detection using event data in
autonomous driving, showcasing the competitive benefits of event cameras.

事件相机在自动驾驶中利用事件数据进行目标检测具有竞争优势。该论文提供了事件相机在自动驾驶中目标检测的概述，强调了事件相机的低延迟和轻量级架构的竞争性优势。

自动驾驶中基于事件的深度目标检测：综述

Deep Event-based Object Detection in Autonomous Driving: A Survey

Real-time video segmentation is a crucial task for many real-world
applications such as autonomous driving and robot control. Since
state-of-the-art semantic segmentation models are often too heavy for real-time
applications despite their impressive performance, researchers have proposed
lightweight architectures with speed-accuracy trade-offs, achieving real-time
speed at the expense of reduced accuracy. In this paper, we propose a novel
framework to speed up any architecture with skip-connections for real-time
vision tasks by exploiting the temporal locality in videos. Specifically, at
the arrival of each frame, we transform the features from the previous frame to
reuse them at specific spatial bins. We then perform partial computation of the
backbone network on the regions of the current frame that captures temporal
differences between the current and previous frame. This is done by dynamically
dropping out residual blocks using a gating mechanism which decides which
blocks to drop based on inter-frame distortion. We validate our
Spatial-Temporal Mask Generator (STMG) on video semantic segmentation
benchmarks with multiple backbone networks, and show that our method largely
speeds up inference with minimal loss of accuracy.

本文提出了一个新的框架，利用视频中的时空局部性来加速实时视觉任务中带有跳跃连接的任何结构，通过基于失真的门控机制动态删除残差块来在当前帧的区域上执行骨干网络的部分计算。

适用于实时视频分割的失真感知网络剪枝和特征重用

Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation

Recent success of semantic segmentation approaches on demanding road driving
datasets has spurred interest in many related application fields. Many of these
applications involve real-time prediction on mobile platforms such as cars,
drones and various kinds of robots. Real-time setup is challenging due to
extraordinary computational complexity involved. Many previous works address
the challenge with custom lightweight architectures which decrease
computational complexity by reducing depth, width and layer capacity with
respect to general purpose architectures. We propose an alternative approach
which achieves a significantly better performance across a wide range of
computing budgets. First, we rely on a light-weight general purpose
architecture as the main recognition engine. Then, we leverage light-weight
upsampling with lateral connections as the most cost-effective solution to
restore the prediction resolution. Finally, we propose to enlarge the receptive
field by fusing shared features at multiple resolutions in a novel fashion.
Experiments on several road driving datasets show a substantial advantage of
the proposed approach, either with ImageNet pre-trained parameters or when we
learn from scratch. Our Cityscapes test submission entitled SwiftNetRN-18
delivers 75.5% MIoU and achieves 39.9 Hz on 1024x2048 images on GTX1080Ti.

本文提出了一种基于 light-weight general purpose architecture、light-weight upsampling 以及融合多重分辨率的特征来扩大接受域的语义分割方法，在多个数据集上获得显着的优越性能和较高的 MIoU 和帧率。