The rapid expansion of car ownership worldwide has further raised the importance of vehicle safety. The reduced cost of cameras and optical devices has made it economically feasible to deploy front-mounted intelligent systems for visual-based event detection for forward collision avoidance and mitigation. While driving at night, vehicles in front are generally visible by their tail lights. The turn signals are particularly important because they signal lane change and potential collision. Therefore, this paper proposes a novel visual-based approach, based on the Nakagami- m distribution, for detecting turn signals at night by scatter modeling of tail lights. In addition, to recognize the direction of turn signals, reflectance is decomposed from the original image. Rather than using knowledge of heuristic features, such as the symmetry, position, and size of the rear-facing vehicle, we focus on finding the invariant features to model turn signal scattering by Nakagami imaging and therefore, conduct the detection process in a part-based manner. Experiments on an extensive data set show that our proposed system can effectively detect vehicle braking under different lighting and traffic conditions, and thus, demonstrates its feasibility in real-world environments.