This letter investigates the performance of energy detector with multiple antennas in cognitive vehicular networks, where the sensing signals of the secondary user are temporally correlated. A novel analytical method, based on new results of confidence interval estimation for auto-correlated matrix in random matrix theory, is proposed to estimate the miss detection probability. The estimate accuracy is verified via simulations. Based on such results, the impacts of signal temporal correlation on sensing performance and decision threshold are analyzed.