As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.

Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.

Minimally invasive diagnosis and interventions provide many benefits such as higher efficiency, safer, minimum pain, quick recovery etc. over conventional way for many procedures. Large robots such as da-Vinci are being used in this purpose, whereas research of miniature robots for laparoscopic and endoscopic use, is growing in the recent years. A comprehensive literature search is performed using keywords’ laparoscopic robot, capsule endoscope, surgical medical robot etc. primarily for the time period of 2000–2015. The articles relevant to the theme of the paper are reviewed and included in the paper. This paper concentrates medical robots for minimally invasive diagnosis and intervention in general and propulsions of miniature robots in particular. Robots are classified and compared using critical characteristics and summarized in Table 1, Table 2, Table 3, Table 4, Table 5 and Table 6. Large robots such as da-Vinci are successfully used in many procedures e.g. neurosurgery, cardiothoracic surgery etc. However there are needs for more functionality which might lead to flexible robots. For miniature robots, each propulsion mechanism has some advantages and disadvantages. While external magnetic propulsions have potential to provide propulsion without increasing the robot size, they lack precise position control and may require expensive and bulky equipment. On the other hand internal propulsions have the capability of precise position control but require mechanisms which need substantial amount of power to drive. Hybrid propulsion which combines best features of both internal and external propulsions could be a solution for this. Robots have improved the healthcare services for many medical procedures. However, still there are challenges to address to enable use of medical robots universally inside and outside hospitals for diagnosis and interventions