Bayesian Neural Networks (BNNs) provide valid uncertainty estimation on their feedforward outputs. However, it can become computationally prohibitive to apply them to modern large-scale neural networks. In this work, we combine Laplace approximation with linearized inference for a real-time and robust uncertainty evaluation. Specifically, we study the effectiveness and computational necessity of a diagonal Hessian approximation in Laplace approximation on over-parameterized networks. The proposed approach is investigated on object detection tasks in an autonomous driving scenario and demonstrates faster inference speed and convincing results.