In　recent　years,　speaker-independent,　monaural　speech　separation　methods　have　made　great　progress　with　the　development　of　deep　neural　networks　(DNNs).　However,　in　the　automotive　environment,　computational　resources　are　limited　and　the　cockpit　sound　field　environment　is　more　complex.　These　factors　pose　significant　challenges　to　the　task　of　speech　separation.　To　address　these　challenges,　this　paper　proposes　a　parallel-path　transformer　model.　The　model　employs　a　parallel　processing　strategy　that　combines　improved　feed-forward　networks　with　transformer　modules.　It　simultaneously　applies　intra-chunk　transformer　and　inter-chunk　transformer　to　process　the　input　sequences,　avoiding　the　need　for　implicit　modeling　of　intermediate　states.　This　approach　enables　the　model　to　perform　local　and　global　modeling　of　the　speech　signals　in　a　parallel　manner,　capturing　both　short　and　long-term　dependencies　within　the　speech　sequences.　Consequently,　the　proposed　model　enhances　the　system＇s　modeling　performance.　Experimental　results　on　the　WSJ0-2Mix　and　WHAM!　datasets　demonstrate　that　the　proposed　model　achieves　excellent　speech　separation　performance　while　maintaining　smaller　model　parameters　and　computational　complexity.　©　2023　IEEE.