Multi-target tracking (MTT) methods estimate the trajectory of targets from noisy measurement; therefore, they can be used to handle the pedestrian-vehicle interaction for a moving vehicle. MTT has an important part in assisting the Automated Driving System and the Advanced Driving Assistance System to avoid pedestrian-vehicle collisions. ADAS and ADS rely on correct estimates of the pedestrians' position and velocity, to avoid collisions or unnecessary emergency breaking of the vehicle. Therefore, to help the risk evaluation in these systems, the MTT needs to provide accurate and robust information of the trajectories (in terms of position and velocity) of the pedestrians in different environments. Several factors can make this problem difficult to handle for instance in crowded environments the pedestrians can suffer from occlusion or missed detection. Classical MTT methods, such as the global nearest neighbour filter, can in crowded environments fail to provide robust and accurate estimates. Therefore, more sophisticated MTT methods should be used to increase the accuracy and robustness and, in general, to improve the tracking of targets close to each other. The aim of this master's thesis is to improve the situational awareness with respect to pedestrians and pedestrian-vehicle interactions. In particular, the task is to investigate if the GM-PHD and the GM-CPHD filter improve pedestrian tracking in urban environments, compared to other methods presented in the literature.  The proposed task can be divided into three parts that deal with different issues. The first part regards the significance of different clustering methods and how the pedestrians are grouped together. The implemented algorithms are the distance partitioning algorithm and the Gaussian mean shift clustering algorithm. The second part regards how modifications of the measurement noise levels and the survival of targets based on the target location, with respect to the vehicle's position, can improve the tracking performance and remove unwanted estimates. Finally, the last part regards the impact the filter estimates have on the tracking performance and how important accurate detections of the pedestrians are to improve the overall tracking. From the result the distance partitioning algorithm is the favourable algorithm, since it does not split larger groups. It is also seen that the proposed filters provide correct estimates of pedestrians in events of occlusion or missed detections but suffer from false estimates close to the ego vehicle due to uncertain detections. For the comparison, regarding the improvements, a classic standard MTT filter applying the global nearest neighbour method for the data association is used as the baseline. To conclude; the GM-CPHD filter proved to be the best out of the two proposed filters in this thesis work and performed better also compared to other methods known in the literature. In particular, its estimates survived for a longer period of time in presence of missed detection or occlusion. The conclusion of this thesis work is that the GM-CPHD filter improves the tracking performance and the situational awareness of the pedestrians.