1. A computer vision method for autonomous driving that uses only a single camera, comprising: moving object localization in 3D with a real-time framework that harnesses object detection and monocular structure from motion (SFM) through a ground plane estimation; tracking feature points on moving cars a real-time framework to and use the feature points for 3D orientation estimation; correcting scale drift with ground plane estimation that combines cues from sparse features and dense stereo visual data; and applying a Kalman filter framework that adapts the fusion observation covariances at every frame to reflect the relative uncertainty of each cue, the Kalman filter comprising a model of state of evolution as 
 x k =Ax k-1 +w k-1 ,p ( w ): N (0 ,Q ), 
 z k =Hx k-1 +v k-1 ,p ( v ): N (0 ,U ), where x represents a state variable, z an observation, Q and U represent covariances of process and observation noise and are assumed to be zero mean multivariate normal distributions as indicated by p(w):N(0,Q) and p(v):N(0,U), A represents state transition, w a process noise, H an observation that maps true state space into observed state space, and v an observation noise.