Homonymous hemianopia (HH) patients report difficulties in avoiding collisions with other pedestrians. We evaluated pedestrian collision detection and avoidance behaviors in HH patients and healthy controls using a novel virtual reality (VR) walking with pedestrians, which enables natural walking behavior in an empty real-world corridor while viewing an immersive VR environment (shopping mall with colliding and other pedestrians) presented in a head-mounted display (HMD). Critically, it measures avoidance maneuvers in addition to collision detection. Colliding and non-colliding pedestrian scenarios were developed for Meta Quest 2 using Unity. Ten normal vision (NV) subjects and 12 HH subjects detected and avoided collisions with virtual approaching and overtaken pedestrians initialized at bearing angles of 20, 40, and 60 degrees, with planned time-to-collision of 6 seconds in each trial. HH subjects were less likely to detect and more likely to collide with pedestrians than NV, particularly for blind-side targets. Response times did not differ between groups but were faster for overtaken pedestrians. HH subjects also biased their head rotations toward the blind side and more after detection compared to before. Collision avoidance difficulties as reported by HH subjects, which clinical measures fail to capture, were recorded and analyzed with objective measures. These metrics may offer further insights into the underlying mechanisms driving collision avoidance behaviors. Our HMD-VR collision detection and avoidance paradigm enables natural walking behaviors and offers an affordable, objective assessment tool that may be adopted by clinicians for mobility enhancement and rehabilitation.