With　the　increasing　complexity　of　urban　roads　and　rising　traffic　flow,　traffic　safety　has　become　a　critical　societal　concern.　Current　research　primarily　addresses　drivers＇　attention,　reaction　speed,　and　perceptual　abilities,　but　comprehensive　assessments　of　cognitive　abilities　in　complex　traffic　environments　are　lacking.　This　study,　grounded　in　cognitive　science　and　neuropsychology,　identifies　and　quantitatively　evaluates　ten　cognitive　components　related　to　driving　decision-making,　execution,　and　psychological　states　by　analyzing　video　footage　of　drivers＇　actions.　Physiological　data　(e.g.,　Electrocardiogram　(ECG),　Electrodermal　Activity　(EDA))　and　non-physiological　data　(e.g.,　Eye　Tracking　(ET))　are　collected　from　simulated　driving　scenarios.　A　dual-branch　Transformer　network　model　is　developed　to　extract　temporal　features　from　multimodal　data,　integrating　these　features　through　a　weight　adjustment　strategy　to　predict　driving-related　cognitive　abilities.　Experiments　on　a　multimodal　driving　dataset　from　the　Computational　Physiology　Laboratory　at　the　University　of　Houston,　USA,　yield　an　Accuracy　(ACC)　of　0.9908　and　an　F1-score　of　0.9832,　confirming　the　model＇s　effectiveness.　This　method　effectively　combines　scale　measurements　and　driving　behavior　under　secondary　tasks　to　assess　cognitive　abilities,　providing　a　novel　approach　for　driving　risk　assessment　and　traffic　safety　strategy　development.