# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 1 Introduction  
The rise in the development of autonomous vehicles underpins essential safety concerns particularly for vulnerable road users (VRUs) such as pedestrians and cyclists. Concerns have been mounting specifically surrounding whether the autonomous vehicle is able to take them into consideration while operating on public roads. Therefore, it is critical that the autonomous vehicle can detect, classify and predict the intention of the VRUs in real time, and required action is taken not to compromise the safety of other road users. To achieve this, deep learning (DL) techniques have recently been employed for detection and pose estimation to predict the intention of pedestrians and cyclists. For example, Convolutional Neural Networks (CNNs), a type of DL technique, have been highly successful in the field of object detection, particularly, pedestrian detection [1; 2; 3; 4; 5]. Recent advances of such DL techniques have outperformed previous methods of computer vision problems (see [6; 7; 8; 9; 10] reduced number of refs). Some DL techniques used for pedestrian detection have achieved miss rates of less than 10% [11]. Although the miss rate is significantly low, they are yet to reach human levelsof detection, and therefore significant research is still necessary. Until detection levels are improved, autonomous vehicles remain a danger to VRUs. According to the World Health Organisation (WHO), nearly half of road traffic fatalities are experienced by pedestrians and cyclists than any other road users as they do not have any special means of protection (i.e., helmets, clothing, etc.) [12]. To be able to predict the intention of a pedestrian using identification and pose estimation techniques would provide a higher level of safety for all road users. In 2013, WHO reported that it is expected that traffic accidents will be the fifth leading cause of death by 2030, rising from the current eighth position [13; 14]. In 2013, VRUs make up more than a quarter of victims of traffic accidents. Of the deaths recorded due to traffic accidents, 42% were pedestrians and 16% were cyclists, with 69% of these fatal accidents occurring in urban locations. In 2017, of all fatalities due to road traffic accidents, 21% were pedestrians, and 8% were cyclists [15]. In the UK, pedestrians and cyclists accounted for 26% and 6% of road traffic fatalities, respectively [16] in 2017. Most accidents occurred in rural roads (55%) and Urban areas (37%). It is also worth noting that half of the accidents involving pedestrians occur at night [17; 18]. Autonomous vehicles aim to make the roads safer for the VRUs through accurate detection. Although detection systems have become more accurate, they have yet to reach human levels. To improve the accuracy of detection systems, the challenges that need to be overcome include occlusion, crowding, weather and lighting conditions. The flowchart in Figure 1 represents the tasks required by the autonomous vehicles to safely detect and estimate the future actions of VRUs. This process allows the vehicle to safely navigate with respect to the VRU. The interaction between the autonomous vehicle and its surroundings is achieved via sensors which collect information primarily to detect and track objects. The sensor input in Figure 1 is affected by external sources, which can reduce efficiency. Typically, the sensing method relies on a vision-based approach such as visible band cameras (operating at the spectrum of 400 nm to 700 nm) [1; 2; 3; 4]. Sensing based on the visible light spectrum is susceptible to ambient light, shadowing and weather conditions. During low-light conditions due to the time of the day, weather, shadowing, etc. can reduce the accuracy of the sensors. A common approach to overcome this problem is to create multiple sensor systems using sensor fusion (e.g., combining visible and infrared band camera) to increase the robustness and accuracy [3; 4].