SAFE-DRIVE

Accepted Papers

Full Papers

Pedestrian Groups Matter: Unraveling their Impact on Pedestrian Crossings when Interacting with an Automated Vehicle
Maximilian Hübner, Martina Baude, and Klaus Bengler
Abstract

Since future mobility will change into mixed traffic,the communication approach of automated vehicles should consider multiple human receivers. This virtual reality study investigates the external communication of an automated vehicle in interactions with multiple pedestrians. Involving 42 participants, the study explores the impact of a simulated pedestrian group at the roadside, a seldom-explored aspect in current research on human-vehicle interactions. Results reveal a significant influence of the additional pedestrian group on participants' behaviors and perceptions, emphasizing the need for nuanced communication strategies in automated vehicle design. This paper contributes to understanding social factors in automated vehicle interactions, providing valuable insights for future research and the development of human-centric automated vehicle communication systems.

Pedestrian Safety by Intent Prediction: A Lightweight LSTM-Attention Architecture and Experimental Evaluations with Real-World Datasets
Afnan Alofi, Ross Greer, Akshay Gopalkrishnan and Mohan Trivedi
Abstract

Autonomous vehicles face significant challenges in understanding pedestrian behavior, particularly in urban environments. In such settings, the system must recognize pedestrian intentions and anticipate their actions to achieve safe and intelligent driving. This paper focuses on predicting pedestrian crossings, enabling oncoming vehicles to react to pedestrians in a traffic scene in a timely manner. We investigate the effectiveness of various input features for pedestrian crossing prediction, including human poses, bounding boxes, and ego vehicle speed features. We propose a novel lightweight architecture based on LSTM and attention to accurately identify crossing pedestrians. Our methods are evaluated on two widely used public datasets for pedestrian behavior, PIE and JAAD datasets, and our algorithm achieves a state-of-the-art performance in both datasets by reaching a prediction accuracy of 91% and an F1-score of 84% on the PIE dataset and an accuracy of 67% and an F1-score of 77% on the JAAD Behavior (BEH) split.

In Search of Social Presence: Evoking an Impression of Real Pedestrian Behavior Using Motion Capture
Chantal Himmels, Jakob Peintner, Carina Manger, Teresa Rock, Oliver Jung,and Andreas Riener
Abstract

Virtual Reality (VR) is commonly utilized to examine driver interactions with vulnerable road users (VRUs) in an effective and secure manner. Recent studies, however, have highlighted issues in VR simulations, particularly concerning the authenticity of state-of-the-art pedes- trian agent behaviors. These inaccuracies can compromise the perceived realism of the situation, potentially leading to unrepresentative driver reactions. This paper aims to showcase enhancements in pedestrian agent models and evaluate their subsequent advantages. To this end, real pedestrian movements, captured via motion-capture technology, were compared with outputs from a contemporary pedestrian agent model within a VR driving simulator experiment. The findings underpin the advantages of using motion-captured pedestrians to enhance social presence. Additionally, participant feedback emphasized that certain elements, such as head movements, explicit gestures, and subtle cues like hesitation before entering the road, were crucial in distinguishing realistic from unrealistic agents. These insights contribute significantly to refining the focus for systematic advancements in (pedestrian) agent models in VR environments. Such improvements are pivotal in augmenting the users sense of presence and the behavioral accuracy of the simulations.

Connected and Automated Transportation System in Multi-Agent Environment
Angah, Ohay, Zhang, Yiran and Ban, Xuegang (Jeff)
Abstract

Traffic simulation is important for transportation researchers, analysts, and policymakers. It can be used to test vehicle/traffic control algorithms, gain insights into traffic dynamics, and develop traffic management strategies that can improve the efficiency and safety of transportation systems. Unfortunately, many existing simulation platforms have limitations to cater to diverse simulation scales. This study presents a comprehensive multiscale vehicle-traffic-demand (VTD) simulation platform tailored for connected and automated transportation systems. This platform integrates Unity 3D, Simulation of Urban Mobility (SUMO), and Multiagent Transport Simulation (MATSim) to facilitate an in-depth analysis of both micro and macro-level traffic behaviors. A critical aspect of our work involves the meticulous setup and calibration of traffic networks in Greater and Downtown Seattle, ensuring effective integration and communication between the various simulation tools. This advanced platform not only serves as a robust tool for testing and refining vehicle/traffic control algorithms but also opens new avenues for research into traffic dynamics learning and the development of sophisticated traffic control solutions.