Object　detection　in　autonomous　driving　scenarios　has　become　a　popular　task　in　recent　years.　Due　to　the　high-speed　movement　of　vehicles　and　the　complex　changes　in　the　surrounding　environment,　objects　of　different　scales　need　to　be　detected,　which　places　high　demands　on　the　performance　of　the　network　model.　Additionally,　different　driving　devices　have　varying　performance　capabilities,　and　a　lightweight　model　is　needed　to　ensure　the　stable　operation　of　devices　with　limited　computing　power.　To　address　these　challenges,　we　propose　a　lightweight　network　called　BiGA-YOLO　based　on　YOLOv5.　We　design　the　Ghost-Hardswish　Conv　module　to　simplify　the　convolution　operations　and　incorporate　spatial　coordinate　information　into　feature　maps　using　Coordinate　Attention.　We　also　replace　the　PANet　structure　with　the　BiFPN　structure　to　enhance　the　expression　ability　of　features　through　different　weights　during　the　process　of　fusing　multi-scale　feature　maps.　Finally,　we　conducted　extensive　experiments　on　the　KITTI　dataset,　and　our　BiGA-YOLO　achieved　a　mAP@0.5　of　92.2%　and　a　mAP@0.5:0.95　of　68.3%.　Compared　to　the　baseline　model　YOLOv5,　our　proposed　model　achieved　improvements　of　1.9%　and　4.7%　in　mAP@0.5　and　mAP@0.5:0.95,　respectively,　while　reducing　the　model　size　by　15.7%　and　the　computational　cost　by　16%.　The　detection　speed　was　also　increased　by　6.3　FPS.　Through　analysis　and　discussion　of　the　experimental　results,　we　demonstrate　that　our　proposed　model　is　superior,　achieving　a　balance　between　detection　accuracy,　model　size,　and　detection　speed.