Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the potential to radically change the way we travel. Many such vehicles currently rely on segmentation and object detection algorithms to detect and track objects around its surrounding. The data collected from the vehicles are often sent to cloud servers to facilitate continual/life-long learning of these algorithms. Considering the bandwidth constraints, the data is compressed before sending it to servers, where it is typically decompressed for training and analysis. In this work, we propose the use of a learning-based compression Codec to reduce the overhead in latency incurred for the decompression operation in the standard pipeline. We demonstrate that the learned compressed representation can also be used to perform tasks like semantic segmentation in addition to decompression to obtain the images. We experimentally validate the proposed pipeline on the Cityscapes dataset, where we achieve a compression factor up to $66 \times$ while preserving the information required to perform segmentation with a dice coefficient of $0.84$ as compared to $0.88$ achieved using decompressed images while reducing the overall compute by $11\%$.

翻译：自动驾驶汽车和高级驾驶辅助系统（ADAS）有望彻底改变我们的出行方式。目前，许多此类车辆依赖分割和物体检测算法来感知并追踪周围环境中的物体。从车辆收集的数据通常被发送至云服务器，以支持这些算法的持续/终身学习。考虑到带宽限制，数据在传输前需进行压缩，而在服务器端通常需要解压缩以用于训练和分析。本研究提出使用基于学习的压缩编解码器，以减少标准流程中因解压缩操作产生的延迟开销。我们证明，除了用于图像重建的解压缩功能外，学习型压缩表征还可直接用于执行语义分割等任务。我们在Cityscapes数据集上对所提流程进行了实验验证，在保留执行分割所需信息的前提下，实现了高达66倍的压缩比，分割结果的Dice系数为0.84（相比之下，使用解压缩图像时达到0.88），同时整体计算量降低了11%。