Autonomous vehicles use different sensors to estimate their surroundings, one of which is a stereo camera. However, the efficiency of this sensor in unstructured and heterogeneous traffic has not been studied. This paper discusses and evaluates some state-of-the-art depth estimation algorithms alongside traditional stereo-matching algorithms. The algorithms have been evaluated in various weather conditions and times of the day. The environment contained unstructured and heterogeneous traffic elements such as cyclists, dense traffic, two-wheeled vehicles, and random pedestrians. The paper considers different methods for stereo-matching and generating disparity maps. To ensure that the results were produced in diverse scenarios, the stereo depth estimation algorithms were evaluated on ApolloScape, and data was generated from CARLA.