Mixed Reality (MR) systems using Head-Up Displays (HUDs) offer promising approaches for driver assistance without diverting visual attention from the road. However, unlike aviation applications where pilots receive mandatory HUD training, consumer vehicle drivers lack such preparation. This raises concerns about effective and error-robust HUD design. In this paper, an exploratory study is presented where two types of virtual lane augmentations in HUDs are empirically evaluated under low visibility driving conditions. Participants completed a within-subject experimental design comparing unsupported baseline driving with two HUD conditions: center-HUD (virtually highlighting the lane center) and right-edge-HUD (highlighting the road edge). A total of six intentional augmentation errors (three for each augmentation type) were introduced to assess participant’s error detection capabilities. Results demonstrate that both HUD augmentation types significantly improved driving precision compared to unsupported baseline drives. However, center-HUD augmentation led to delayed error detection that might have been caused by attentional tunneling: As preliminary eye-tracking data suggests, participants were fixating primarily on the virtual center line, leading to reduced situational awareness. Right-edge-HUD augmentation also improved driving precision significantly while enabling faster error detection. Participant’s subjective evaluations strongly favored right-edge augmentation over center-HUD. The findings reported reveal a critical trade-off in mixed-reality driver assistance design: while center augmentation enhances precision, it might increase vulnerability to system malfunctions. Road edge augmentation, in comparison, seem to better preserve situational awareness. The results suggest important implications for automotive MR system development The findings suggest relevant implications for the design of automotive MR-based HUDs. .