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Enhancing Driving Efficiency Through Pedestrian Motion Understanding

Research focuses on improving driving efficiency in shared spaces by modeling vehicle-crowd interaction and utilizing sensor data. Methods include offline simulation and online adjustments for real-time pedestrian detection. The project also introduces datasets for controlled experiments and campus scenarios to enhance understanding and prediction accuracy.

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Enhancing Driving Efficiency Through Pedestrian Motion Understanding

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  1. Understanding and Guiding Pedestrian and Crowd Motion for Improving Transportation (Driving) Efficiency Lead Researcher: ÜmitÖzgüner Project Team: : ÜmitÖzgüner, Dongfang Yang

  2. Project: Understanding and Guiding Pedestrian and Crowd Motion for Improving Transportation (Driving) Efficiency Goal: improving transportation (driving) efficiency in shared spaces A variety of sensors (stereo cameras, mono cameras, lidars, infrastructure sensors) to detect pedestrian motion Vehicle-crowd interaction (VCI) models to predict pedestrian motion Applications in vehicle in combination with VCI models Offline approach: use simulation results in similar scenarios to guide the autonomous vehicle to generate a better driving strategy Case study: using regression method to minimize the time of passing a pedestrian-dense area Online approach: the autonomous vehicle constantly adjusts its driving strategy based on the immediate detection of pedestrians Case study: applying model predictive control to regulate longitudinal speed of a vehicle in pedestrian-dense area

  3. Project: Understanding and Guiding Pedestrian and Crowd Motion for Improving Transportation (Driving) Efficiency Modeling Vehicle-Crowd Interaction (VCI) Goal: describing the collective behavior of a crowd of pedestrians under the influence of an approaching vehicle Improvement on social force model Added vehicle influence on pedestrians, which is not available in most existing models Adjusted/re-designed pedestrian-pedestrian interaction mechanism such that the vehicle influence can be incorporated Calibrated the proposed model by using Genetic Algorithm (GA) based on the dataset of fundamental VCI scenarios Validated the proposed model by evaluating the simulation result. Modified individual forces in social force model: force from environment force from nearby pedestrians force from destination force from the vehicle An example of vehicle influence (force) on a pedestrian walking from +y to -y: (1) contours - force magnitude; (2) arrows - force directions back/front interaction lateral interaction

  4. Project: Understanding and Guiding Pedestrian and Crowd Motion for Improving Transportation (Driving) Efficiency DatasetDevelopment We provide two new datasets: CITR Dataset: controlled experiments including fundamental VCI DUT Dataset: everyday campus scenarios including natural VCI Our dataset covers vehicle-crowd interaction: Use drone as recording equipment – eliminate possible occlusions Robust tracking techniques (CRST) – automatically (need initial position) obtain accurate trajectories Kalman Filters for trajectory refinement: point-mass model for pedestrians, bicycle model for vehicles CITR Dataset DUT Dataset Two locations: - Crosswalk at unsignalized intersection - A shared space near a roundabout Controlled experiments 6 scenarios that can be pairwise compared and analyzed • CITR dataset on GitHub: https://github.com/dongfang-steven-yang/vci-dataset-citr • DUT dataset on GitHub: https://github.com/dongfang-steven-yang/vci-dataset-dut

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