Lots and lots of research has been done on this very topic for decades. There are already many apps and devices with audio feedback and haptic feedback. If you ask everyone in your group to research a different slice of what's already on the market--apps, ultrasound devices, smart glasses, smart canes (ugh), etc.--you'll find out a lot in a week.
Grab a copy of Foundations of Orientation & Mobility, look up the references, and keep going! It's good to try to solve a problem with your group just to see what you devise first, but don't get too far before you read about what's been done so you don't end up reinventing the wheel. Whole careers have been spent creating and testing navigation and safety systems, and you'll benefit a lot from reading papers by it.
Sensor substitution has been around for half a century. Having newer tech doesn't solve some of the fundamental problems. Read and find out what those problems are.
Some other resources are posted here:
$1 Vibrating motors for someone in motion is something you're right to think could be problematic. For audio . . .well, talk to some blind people locally and you'll quickly figure out there are a few important considerations.
Think very carefully about legal liability associated with detecting street signs and crosswalks. **Consult with a lawyer who specializes in liability.** You're not the first to try outdoor recognition tasks, and there are good reasons why assistive tech companies with engineering teams and large budgets have shied away from it. This is not simply a recognition task (which, incidentally, is even harder than it seems). Consider what happens if your hardware fails at an inopportune moment, or even whether your software development approach can guarantee code that is safe.
I realize this may be a student project, but you mentioned designing a "product," and you could end up spending a lot of effort only to discover some troubling news later. Best to figure out some of the road blocks now.
For sidewalks and other outdoor recognition tasks you should see some initial success, but then you'll run into some very serious problems. Ask yourself what the best recognition accuracy is for **any** deep learning task. Consider whether this is sufficient for safety. (It isn't.)
A lot of assistive tech builds in serious flaws from the start that can't (or at least likely won't) be overcome later. With some research you could likely leapfrog other student projects.