Modern-day cars are equipped with numerous cameras and sensors, typically integrated with advanced decision-control systems that enable the vehicle to perceive its surroundings and navigate autonomously. Efficient processing of data from sensors, lidars, radars and cameras is quite computationally intensive and can not be done with good accuracy using less capable onboard resources. In order to deal with this problem, some computation requirements (also referred as tasks) are offloaded to infrastructure or executed in parallel in both autonomous vehicle (AV) and infrastructure to enhance accuracy. The infrastructure comprises base stations, a centralized cloud, and a CS. Base stations (BSs) execute tasks in collaboration with a significantly more powerful centralized cloud, while the centralised scheduler (CS) centrally schedules all the tasks. The base station receives tasks from multiple AVs, each with varying deadlines, criticality, and locations. Our main goal is to maximize the profit of the infrastructure by (a) minimizing the number of drop tasks, (b) minimizing the distance cost for task offloading, and (c) minimizing the energy usage of BSs. In this work, we proposed efficient approaches to schedule the collection of tasks to the BSs, by employing a hybrid scheduling approach where tasks from AVs get allocated to nearby base stations if the nearby BSs are lightly loaded, otherwise AVs send the task to CS for allocation. The CS maximizes the profit by following strategies: (a) selection of BS considering distance and energy consumption, (b) when task load is moderate or low, highly critical tasks run at favourable utilisation, and (c) low-critical tasks are dropped to free up resources for executing high-critical tasks. Based on our experiments, proposed approaches improved the QoS provided by up to 25% compared to the state-of-the-art approach in real-life datasets.