The fast development of intelligent transport systems requires high-rate communications, high energy efficiency, and low latency. One promising solution to meet the requirements is to adopt the massive multiple-input multiple-output (MIMO) technique. The massive MIMO architecture is attractive to multiple vehicles on the road for vehicle-to-infrastructure access as large-scale antennas can be deployed at the roadside unit. Besides, massive MIMO systems can significantly improve the system spectrum efficiency and energy efficiency. However, the benefits are achieved at the cost of high computational complexity and long processing delay even with linear detection methods. In this paper, we propose low complexity and fast processing algorithms to address those issues. The proposed schemes transform the large-scale matrix inverse problems into solving linear equations. We then introduce iterative methods to solve linear equations. To speed up the updating process in iterative method, we utilize the properties of block matrix, and perform the updating process on a small size block independently. The independent processing progress can be paralleled, which greatly reduces the overall processing time. We also evaluate the performance of the proposed schemes in terms of the probability that the convergence conditions are met, and the system bit error rate. The results show that the proposed schemes achieve good system performance but at low complexity and latency.