# Autonomous Vehicles Technological Trends
###### Abstract
[4] presented that pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way which avoids traffic accidents, which may result in individuals being harmed. In this work, a review of convolutional neural networks (CNNs) to tackle pedestrian detection is presented. We further present models based on CNNs and transfer learning. The CNN model with the VGG-16 architecture is further optimized using the transfer learning approach. This paper demonstrates that the use of image augmentation on training data can yield varying results. In addition, a pre-processing system that can be used to prepare 3D spatial data obtained via LiDAR sensors is proposed. This pre-processing system is able to identify candidate regions that can be put forward for classification, whether that be 3D classification or a combination of 2D and 3D classifications via sensor fusion. We proposed a number of models based on transfer learning and convolutional neural networks and achieved over 98% accuracy with the adaptive transfer learning model. [PERSON] et al. [5] presented the current design paradigm of car cabin components which assumes that seats are aligned with the driving direction. All passengers are aligned with the driver that, until recently, was the only element in charge of controlling the vehicle. The new paradigm of self-driving cars eliminates several of those requirements, releasing the driver from control duties and creating new opportunities for entertaining the passengers during the trip. This creates the need for controlling functionalities that must be closer to each user, namely on the seat. This work proposes the use of low-cost capacitive touch sensors for controlling car functions, multimedia controls, seat orientation, door windows, and others. In the current work, we have reached a proof of concept that is functional, as shown for several cabin functionalities. The proposed concept can be adopted by current car manufacturers without changing the automobile construction pipeline. It is flexible and can adopt a variety of new functionalities, mostly software-based, added by the manufacturer, or customized by the end-user. Moreover, the newly proposed technology uses a smaller number of plastic parts for producing the component, which implies savings in terms of production costs and energy, while increasing the life cycle of the component. [PERSON] et al. [6] presented how with the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automotive Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With SAE-level 5, the concept of a shared autonomous vehicle (SAV) will soon become a reality and mainstream. The main purpose of an SAV is to allow unrelated passengers to share an autonomous vehicle without a driver/moderator inside the shared space. However, to ensure their safety and well-being until they reach their final destination, the active monitoring of all passengers is required. In this context, this article presents a microphone-based sensor system that is able to localize sound events inside an SAV. The solution is composed of a micro-electro-mechanical system (MEMS) microphone array with a circular geometry connected to an embedded processing platform that resorts to field-programmable gate array (FPGA) technology to successfully process in the hardware the sound localization algorithms. This paper has been selected as a Feature Paper. [PERSON] et al. [7] presented that the traffic management challenges in peak seasons for popular destinations such as Madinah city have accelerated the need for and introduction of autonomous vehicles and vehicular ad hoc networks (VANETs) to assist in the communication and alleviation of traffic congestions.