With the advent of new technologies, the world will soon witness fully autonomous vehicles (AVs). AVs implement different layers including: perception, behavior and control. One major component that realizes the objectives of autonomous vehicles is Local Dynamic Map (LDM). Although the objectives of automation levels (0-3) were achieved though the existing standardized LDM, the higher levels of automation for AVs (i.e., Level 4 and Level 5) cannot be achieved owing to the complex dynamics of environment and fully independence from a human driver. The challenges of higher levels of automation include: accurate object detection and environment understanding of complex environments. Sharp turns, complex roundabouts, presences of Vulnerable Road Users, blind spots, and unprecedented events are some of the complex events, which are not captured by the LDM of today. Hence, in this paper, we propose a novel approach of introducing additional layers of information, which are populated through the information from external sources e.g., on-road deployed sensors, edges, etc. We term our contributed LDM as evolved LDM (eLDM). We extensively implement the proposed eLDM by working with IoT middleware, technologies like Thingsboard, Adobe XD and Google Web Designer. We created a new eLDM for vehicle and exploited the IoT middleware for aggregating the local (data collected from on-vehicle deployed sensors) and external (data collected from RSUs). To validate the contribution, we tested: the communication, the features of implemented IoT middleware, interface of the middleware with the implemented eLDM. The experiments validated the proper functioning of the developed components and inter-components interaction. The validation was carried out in the real settings i.e., in the UAEU campus with Golf carts.