We present a new approach for making use of scene or situation context in object detection, aiming for state-of-the-art performance while dramatically reducing computa-tional cost. While existing approaches are inspired by Bayes' rule, training context-independent detectors and combining them with context priors in hindsight, we propose to integrate these context priors already into detector design, through algorithmic choices and/or pre-selection of training examples. Although such restricted detectors will, as a consequence, be valid only in regions compatible with context priors, the corresponding simplification of the object-vs-background de-cision problem will lead to reduced computation time and/or increased detection performance. We verify this experimentally by analyzing vehicle detection performance in a realistically simulated inner-city environment where context priors are defined by a road surface mask obtained from the simulation tool. Comparing a restricted detector, based on horizontal gradient detection refined by neural network confirmation, to a generic HOG+SVM-based approach taking into account the road context prior, we show that the restricted detector shows superior vehicle detection performance at a vastly reduced computational cost. We show qualitative results that permit the conclusion that the restricted detector will perform well on real-world scenes if appropriate road context priors are available.