Aiming at the problem of low accuracy of lane detection in complex environment, a lane detection algorithm based on directional distance transform coupled with multi-particle filter was proposed. Firstly, the four-point perspective mapping method was used to transform the input image into an aerial view, which makes the lane boundary parallel and convenient for lane detection. Oriented distance transform (ODT) was introduced to mark the edge pixels of aerial view to the nearest points in horizontal and vertical directions to find the initial boundary points. Secondly, the lane model was constructed by using the lane center, the angle from the center to the left and the right boundary and the tangent angle of the left and the right lane boundary. Two independent 4D particle spaces were applied to the left and the right lane boundary. Subsequently, a multi-particle filter is introduced into the lane model to detect and track a pair of lane boundary points using particles propagating independently on both sides of the lane, and the boundary points are adjusted by local linear regression. In order to optimize the performance of multi-particle filter, dynamic dependencies were created according to the particle state vector. Finally, the weight of particles is determined by iteration, and the lane line was detected by multi-particle filter. Experiments show that, compared with the current popular lane detection algorithms, the proposed algorithm has higher detection accuracy and robustness in a variety of complex interference environments.