This　paper　introduces　a　transformative　edge　computing-based　approach　for　enhancing　driver　attention　and　road　safety　using　EEG-driven　deep　reinforcement　learning　(DRL).　As　driver　inattention　remains　a　significant　factor　in　accidents,　real-time　cognitive　state　monitoring　enabled　by　in-vehicle　edge　devices　offers　new　promise.　Our　method　leverages　EEG　data　collected　from　drivers　using　headsets,　analyzing　signals　related　to　visual　attention.　Edge　computing　resources　in　the　vehicle　extract　features　and　classify　attention　levels　using　Deep　Q-Network　(DQN)　and　Proximal　Policy　Optimization　(PPO)　models　trained　to　approximate　optimal　driving　decisions.　A　novel　reward　structure　combining　driving　performance　and　attention　guides　the　models.　Our　edge　computingpowered　framework　reacts　within　critical　time　latencies　to　maximize　attention　through　interventions　adapting　to　the　driving　environment.　Results　demonstrate　the　effectiveness　of　this　approach,　with　PPO　agent　on　edge　devices　achieving　high　average　rewards　up　to　489,752.4　and　99.3%　reward　as　accuracy　in　classifying　attention　states,　thereby　significantly　outperforming　traditional　methods.　This　underscores　edge　computing＇s　potential　to　enable　real-time　integration　of　neuroscience　and　AI,　advancing　road　safety.　The　edge　resources　deliver　time-critical　analysis　and　adaptation,　while　connectivity　to　the　fog　and　cloud　allows　optimizing　and　learning　at　scale　across　populations.　This　research　pioneers　a　new　epoch　for　road　safety　powered　by　edge　intelligence.