Real-time status updates are playing a key role in the emergence of autonomous driving. Due to the limited and dynamic environment, the vehicle communications may not guarantee the required quality of service (QoS) on demand. In this paper, we consider the intersection scenario with relatively heavy traffic and slightly higher risk, where the base station (BS) remotely controls multiple vehicles. The vehicles sense their own and surrounding contextual information through sensors and send them to the BS through the uplink. Since the urgency of different status information is distinct, the analytic hierarchy process (AHP) method is used to give each status information a context-aware weight so that emergency vehicles can be scheduled first by the BS. Then, age of information (AoI) is also exploited to describe the time elapsed since the generation of the status information obtained from the perspective of the BS. On this basis, the Lyapunov method is considered to optimize the average weighted AoI subject to the limited throughput constraint. Finally, a scheduling strategy based on dynamic domain value is proposed to update vehicles in real time in the long run, so as to minimize the average weighted AoI. The simulation results show that the context-aware weight proposed in this paper has a significant impact on scheduling, and the average weighted AoI of the whole system is optimized compared with other approaches.