Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Abstract

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.

Impact of Safety Message Generation Rules on the Awareness of Vulnerable Road Users

“…Moreover, the strict formulation of these rules may be not applicable for dynamic scenarios; indeed, the urban mobility of cars and VRU may lead to an excessive transmission of messages or a reduction in the awareness due to over filtering mechanisms. Several existing contributions emphasize the importance of the VRU intention recognition for the safety of VRU and the prevention of accidents [ 42 ]; VRU intention can be also used to help in the filtering process of messages. In the following, we briefly describe the key existing contributions that could help to develop message generation rules while taking into account the VRU context, intention, and dynamism.…”

Visibility-Based Technologies and Methodologies for Autonomous Driving

“…vehicles, pedestrians, and cyclists) are obstructed by some obstacles (Figure 17a), the AV may not identify the objects at a safe distance, which may lead to a collision. It is quite challenging for AV vehicles to predict where pedestrians or cyclists may be present in a complex road scene in real-time [30]. In this case, it is meaningful to identify the positions where road users may exist in real-world using dense MLS points and investigate the visibility of these locations to the AV in advance (see Figure 17a and b).…”

Sky Monitoring System for Flying Object Detection Using 4K Resolution Camera

“…Methods employing images [ 8 , 9 , 10 , 11 ] have likewise been proposed, as we can see from the fact that image processing is used in various fields [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. These methods have gained superiority, as they do not affect the monitoring targets or the surrounding environment and allow for systems to be built at a comparatively lower cost.…”