This　study　presents　an　emergency　obstacle　avoidance　control　(OAC)　algorithm　for　intelligent　vehicles　equipped　with　X-by-wire　(XBW)　systems.　A　tire-road　friction　coefficient　(TRFC)　observer　is　developed　based　on　vehicle　and　tire　models.　A　multi-sensor　fusion　method,　utilizing　Kalman　filter　and　Bayesian　estimation,　is　proposed　to　improve　perception　accuracy　by　using　Mahalanobis　distance　to　match　sensor　measurements.　OAC　strategies　are　designed　by　dividing　braking　and　steering　areas　based　on　relative　longitudinal　distance.　The　braking-based　strategy　generates　a　desired　deceleration　which　will　be　realize　by　an　electro-hydraulic　braking　system　(EHBS)　with　a　sliding　mode　controller.　The　steering-based　strategy　plans　an　optimal　trajectory　using　a　cost　function,　generating　a　desired　steering　angle　to　follow　this　trajectory.　The　algorithm　is　tested　on　a　hardware-in-the-loop　(HIL)　system　under　various　scenarios,　demonstrating　the　adaptability　and　precision　of　the　TRFC　observer.　Additionally,　the　proposed　fusion　method　shows　a　significant　improvement　in　the　average　perception　accuracy　of　longitudinal　distance　and　lateral　velocity.　Moreover,　by　considering　TRFC,　the　algorithm　is　able　to　improve　OAC　performance　while　ensuring　driving　comfort　and　vehicle　stability　in　emergency　situations.