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The effectiveness of obstacle avoidance response safety systems such as ADAS, has demonstrated the necessity to optimally integrate and enhance these systems in vehicles in the interest of increasing the road safety of vehicle occupants and pedestrians. Vehicle-pedestrian clearance can be achieved with a model safety envelope based on distance sensors designed to keep a threshold between the ego-vehicle and pedestrians or objects in the traffic environment. More accurate, reliable and robust distance measurements are possible by the implementation of multi-sensor fusion. This work presents the structure of a machine learning based sensor fusion algorithm that can accurately detect a vehicle safety envelope with the use of a HC-SR04 ultrasonic sensor, SF11/C microLiDAR sensor, and a 2D RPLiDAR A3M1 sensor. Sensors for the vehicle safety envelope and ADAS were calibrated for optimal performance and integration with versatile vehicle-sensor platforms. Results for this work include a robust distance sensor fusion algorithm that can correctly sense obstacles from 0.05m to 0.5m on average by 94.33% when trained as individual networks per distance. When the algorithm is trained as a common network of all distances, it can correctly sense obstacles at the same distances on average by 96.95%. Results were measured based on the precision and accuracy of the sensors’ outputs by the time of activation of the safety response once a potential collision was detected. From the results of this work the platform has the potential to identify collision scenarios, warning the driver, and taking corrective action based on the coordinate at which the risk has been identified.