A critical maneuver for a heavy vehicle is braking with different friction on the left and right hand side of the vehicle, called μ-split. This results in an unwanted yaw torque acting on the vehicle. During this situation, the driver maintains the lateral stability and follows the desired path by corrective steering. In anautonomous heavy vehicle the system must handle this situation by itself. The purpose of this thesis is to analyze how an autonomous vehicle can detect a μ-split situation and then use steering control to maintain its path and stability. Two methods for detecting a μ-split situation are presented where one is based on vehicle kinematics, this detector utilizes the difference in wheel speed between the left and right hand side of the vehicle. The other detector is based on lateral vehicle dynamics, this method uses a sliding mode observer to detect unexpected changes in the yaw rate signal. The detectors were tested in a real vehicle and the results showed that the kinematic detector was fast but had a small risk of false detection, while the dynamic detector was slower but more robust. An analysis of the desired steering behavior showed that the steady state during μ-split braking is to drive with a non zero body slip. If a kinematic path follower is used with kinematic error dynamics this will lead to a contradicting behavior since the body slip is equal to the heading error during straight line braking, assuming that the velocity vector of the vehicle is parallel to the path. Simulations showed that during a μ-split situation the Linear Quadratic pathfollower based on kinematic error dynamics manages to follow the path with a non zero body slip while keeping the path errors small. It has also been shown how the detection of a μ-split situation can be used to change control strategy. By introducing active yaw control or change the tuning on the controller after a detection a better result could be achieved.