Pavement distresses and potholes represent road hazards that can cause accidents and damages to vehicles. The latter may vary from a simple flat tyre to serious failures of the suspension system, and in extreme cases to collisions with third-party vehicles and even endanger passengers' lives. The primary scientific aim of this study is to investigate the problem of road hazard detection for driving assistance purposes, towards the final goal of implementing such a technology on future intelligent vehicles. The proposed approach uses a depth sensor to generate an environment representation in terms of 3D point cloud that is then processed by a normal vector-based analysis and presented to the driver in the form of a traversability grid. Even small irregularities of the road surface can be successfully detected. This information can be used either to implement driver warning systems or to generate, using a cost-to-go planning method, optimal trajectories towards safe regions of the carriageway. The effectiveness of this approach is demonstrated on real road data acquired during an experimental campaign. Normal analysis and path generation are performed in post-analysis. This approach has been demonstrated to be promising and may help to drastically reduce fatal traffic casualties, as a high percentage of road accidents are related to pavement distress.