# Application of the Theory of Planned Behavior in Autonomous Vehicle-Pedestrian Interaction
###### Abstract  
Automobile manufacturers, alongside technology providers, researchers, and public agencies, are conducting extensive testing to design autonomous vehicles (AVs) algorithms that will provide a complete understanding of road users, specifically pedestrians. Pedestrian behavior and actions determination are highly unpredictable depending on behavioral beliefs, context, and socio-demographic variables. Context includes everything that potentially affects one's behavior; in AVs-pedestrian interaction, context may consist of weather conditions, road structure, social factors norms, and traffic volume. These influencing elements, therefore, need to be focused on during the development of pedestrian interaction algorithms. For this purpose, the pedestrian behavior questionnaire for FAVs (PBQF) is designed based on the theory of planned behaviour (TPB). A total of almost 1000 voluntary participants completed this multilingual survey. As socio-demographic values and physiological perception varies with local norms, regions, and ethnicity, participants from 27 countries were therefore chosen to account for this variation. One of the key findings of this study is the influence of pedestrian attributes and the context on pedestrian behavior. Pedestrian action cannot be understood without visual observation of the pedestrian themselves and their context. The findings showed that pedestrians build communication with vehicles based on their driving styles. The vehicle's driving style leads pedestrians to think that the vehicle is human-driven or autonomous. The results also revealed that pedestrians use several cues to show their intention. The general perception of AVs was also analyzed, and the communication between AVs and pedestrians with different displaying options was investigated. autonomous vehicle; cross-country comparison; pedestrian context; pedestrian behavior; awareness; adoption; communication mode +
Footnote †: journal:  
The perception process in AVs refers to the vehicle-mounted sensor's understanding of the environment surrounding the vehicle. Curbs, road lanes, obstacles, drivable areas, surrounding vehicles, infrastructure, traffic signs, traffic signals, and road users are all part of environment perception Pedestrians are one of the most vulnerable road users when compared to other road users. A study conducted by [PERSON] [6] identified key factors influencing the adoption of driverless cars; pedestrian interaction was identified as a key factor in AV adoption and public trust. AVs have to prove that they are safer than the average human-driven car and have a lower accident rate. According to a World Health Organization report, approximately 1.35 million people are killed in traffic accidents each year, and 50% of those are vulnerable road users. Further investigation into these accidents reveals that 93% of these fatalities occurred in low and middle-income countries, owing to a number of persistent flaws such as unstructured infrastructure, a lack of vehicle maintenance, and violations of traffic rules [7]. To ensure that AVs are fully robust and capable of acting in any unforeseen circumstances, technologists and automobile manufacturers must ensure that on-road AVs are fully robust and capable of acting in any unforeseen circumstances. The society of automotive engineers (SAE) defined a taxonomy of automation levels with required automation targets, beginning with Level 0 as no automation and ending with Level 5 as full automation [8]. A variety of models, ranging from pedestrian detection to intention estimation and future movement prediction, must collaborate for the full and safe interaction of AVs and pedestrians. In order to achieve this level of maturity, pedestrian model levels are mapped with the level of automation defined by the SAE, ranging from simple driver assistance tools to full driving [9].