Continuous collision detection improves the computation of the contact information for interacting objects in dynamic virtual environments. The computation cost is relatively high in the phase of the elementary test processing. In virtual environments, such as crowds in large urban models, there is a large portion of feature pairs that do not collide but the computation is relatively of high cost. In this paper, we propose a robust approach for solving the scalability of the collision detection problem by applying four distinct phases. First, k-DOPs are used for culling non-proximal triangles. Second, the feature assignment scheme is used for minimizing the number of potentially colliding feature pairs. Third, an intrinsic filter is employed for filtering non-coplanar feature pairs. Forth, we use a direct method for computing the contact time that is more efficient than the numerical Interval Newton method. We have implemented our system and have compared its performance with the most recently developed approaches. Six benchmarks were evaluated and the complexity of the models was up to 1.5M triangles. The experimental results show that our method improves the performance for the elementary tests.