Introduction:　The　proper　execution　of　driving　tasks　requires　information　support.　While　new　technologies　have　increased　the　convenience　of　information　access,　they　have　also　increased　the　risk　of　driver　distraction　and　information　overload.　Meeting　drivers’　demands　and　providing　them　with　adequate　information　are　crucial　to　driving　safety.　Methods:　Based　on　a　sample　of　1,060　questionnaires,　research　on　driving　information　demands　is　conducted　from　the　perspective　of　drivers.　A　principal　component　analysis　and　the　entropy　method　are　integrated　to　quantify　the　driving　information　demands　and　preferences　of　drivers.　The　K-means　classification　algorithm　is　selected　to　classify　the　different　types　of　driving　information　demands,　including　dynamic　traffic　information　demands　(DTIDs),　static　traffic　information　demands　(STIDs),　automotive　driving　status　information　demands　(ATIDs),　and　total　driving　information　demands　(TDIDs).　Fisher＇s　least　significant　difference　(LSD)　is　used　to　compare　the　differences　in　the　numbers　of　self-reported　crashes　among　different　driving　information　demand　levels.　A　multivariate　ordered　probit　model　is　established　to　explore　the　potential　factors　that　influence　the　different　types　of　driving　information　demand　levels.　Results:　The　DTID　is　the　driver＇s　most　in-demand　information　type,　and　accordingly,　gender,　driving　experience,　average　driving　mileage,　driving　skills,　and　driving　style　significantly　affect　the　driving　information　demand　levels.　Moreover,　the　number　of　self-reported　crashes　decreased　as　the　DTID,　ATID,　and　TDID　levels　decreased.　Conclusion:　Driving　information　demands　are　affected　by　a　variety　of　factors.　This　study　also　provides　evidence　that　drivers　who　have　higher　driving　information　demands　are　more　likely　to　drive　more　carefully　and　safely　than　their　counterparts　who　do　not　exhibit　high　driving　information　demands.　Practical　implications:　The　results　are　indicative　of　the　driver-oriented　design　of　in-vehicle　information　systems　and　the　development　of　dynamic　information　services　as　a　way　to　avoid　negative　impacts　on　driving.　©　2023