Video compression in automated vehicles and advanced driving assistance systems is of utmost importance to deal with the challenge of transmitting and processing the vast amount of video data generated per second by the sensor suite which is needed to support robust situational awareness. The objective of this paper is to demonstrate that video compression can be optimised based on the perception system that will utilise the data. We have considered the deployment of deep neural networks to implement object (i.e. vehicle) detection based on compressed video camera data extracted from the KITTI MoSeg dataset. Preliminary results indicate that re-training the neural network with M-JPEG compressed videos can improve the detection performance with compressed and uncompressed transmitted data, improving recalls and precision by up to 4% with respect to re-training with uncompressed data.

<div><div><div><p>Situational awareness based on the data collected by the vehicle perception sensors (i.e. LiDAR, RADAR, camera and ultrasonic sensors) is key for achieving assisted and automated driving functions in a safe and reliable way. However, the data rates generated by the sensor suite are difficult to support over traditional wired data communication networks on the vehicle, hence there is an interest in techniques that reduce the amount of sensor data to be transmitted without losing key information or introducing unacceptable delays. These techniques must be analysed in combination with the consumer of the data, which will most likely be a machine learning algorithm based on deep neural networks (DNNs). In this paper we demonstrate that by compression tuning the DNNs (i.e. transfer learning by re-training with compressed data) the DNN average precision and recall can significantly improve when uncompressed and compressed data are transmitted. This improvement is achieved independently from the compression standard used for compression-training (we used AVC and HEVC), and also when training and transmitted data use the same compression standard or different compression standards. Furthermore, the performance of the DNNs is stable when transmitting data with increasing lossy compression rate, up to a compression ratio of approximately 1200:1; above this value the performance starts to degrade. This work paves the way for the use of compressed sensor data in assisted and automated driving in combination with the optimisation of compression- tuned DNNs.</p></div></div></div>

<div><div><div><p>Situational awareness based on the data collected by the vehicle perception sensors (i.e. LiDAR, RADAR, camera and ultrasonic sensors) is key for achieving assisted and automated driving functions in a safe and reliable way. However, the data rates generated by the sensor suite are difficult to support over traditional wired data communication networks on the vehicle, hence there is an interest in techniques that reduce the amount of sensor data to be transmitted without losing key information or introducing unacceptable delays. These techniques must be analysed in combination with the consumer of the data, which will most likely be a machine learning algorithm based on deep neural networks (DNNs). In this paper we demonstrate that by compression tuning the DNNs (i.e. transfer learning by re-training with compressed data) the DNN average precision and recall can significantly improve when uncompressed and compressed data are transmitted. This improvement is achieved independently from the compression standard used for compression-training (we used AVC and HEVC), and also when training and transmitted data use the same compression standard or different compression standards. Furthermore, the performance of the DNNs is stable when transmitting data with increasing lossy compression rate, up to a compression ratio of approximately 1200:1; above this value the performance starts to degrade. This work paves the way for the use of compressed sensor data in assisted and automated driving in combination with the optimisation of compression- tuned DNNs.</p></div></div></div>