Alam, Md. Golam RabiulFaruq, Sharif Mohammad OmarKhan, WasifShaan, Saif Alam2026-04-122026-04-1220252025-10ID 22101429ID 22101357ID 22101368http://hdl.handle.net/10361/27843Cataloged from PDF version of thesis.Includes bibliographical references (pages 62-66).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.This paper presents a vision-only assistive navigation framework that couples realtime panoptic segmentation with safe reinforcement learning to provide proactive, collision-aware guidance for visually impaired pedestrians in dynamic urban environments using only RGB cameras on resource-constrained devices. The approach integrates a lightweight bottom-up MobileNetV3–FPN panoptic segmentation model for unified scene understanding, a ConvGRU module that predicts short-horizon danger maps from temporal mask sequences, and a multi-input policy that conditions decision-making on both current semantics and anticipated risk. Safety is enforced by a PPO-based controller trained under a constrained formulation (Lagrangian) and supplemented at runtime with an action-shielding safety layer that filters unsafe actions. The system is trained and evaluated in CARLA with domain diversity from Cityscapes and Mapillary Vistas, emphasizing ethical, simulation-first validation and deployment feasibility on edge hardware. Experiments and studies indicate that constrained PPO with action shielding reduces safety violations compared to unconstrained PPO, while ConvGRU-based temporal prediction improves anticipatory avoidance of dynamic obstacles, achieving a favorable speed–accuracy trade-off for wearable use cases with the MobileNetV3 panoptic variant. The work contributes an affordable RGB-only stack, a tightly coupled perception–prediction–control design for proactive safety, and a reproducible benchmark that surfaces limitations in small-object instance quality and sim-to-real transfer, outlining targeted directions for future refinement.66 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Assistive navigationVision-only systemsVisually impairedSafe reinforcementPeople with visual disabilities--Orientation and mobility.Self-help devices for people with disabilities.Assistive computer technology.Real-time data processing.Computer vision.Thesis