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Perception in Automated Vehicles is one of the most crucial tasks, especially at higher levels of automation where the driver no longer needs to monitor the vehicle environment continuously. There are several possibilities of sensor configurations that can support an autonomous vehicle with this task. However the selection and placement of a sensor is not a trivial task. This thesis proposes a framework for evaluation of sensor configurations for an autonomous vehicle with the Full Factorial Design of Experiment (DOE). The first part of the work focuses on the development of an architecture that interacts with CARLA Simulator. The architecture processes sensor data and performs sensor data fusion to generate an environment model, implemented in Robot Operating System (ROS) in a modular fashion such that sensors
can be added or removed to/from the vehicle sensor configuration with ease. The second part of the work focuses on the evaluation of the quality of environment model generated by different sensor configurations in a systematic fashion using a Full Factorial Design of Experiments. Individual sensors are factors in the experiment, and the presence/absence of the sensor and the quality are selected as the levels for the experiment. Parameters evaluating the quality of the environment model are selected as the response variables.
The DOE results allow interpretation of which sensors have a statistically significant impact on the quality of fusion and also the extent of improvement. This allows inference of which combination of sensor will provide a better quality environment model. With the proposed Full factorial Design of Experiments, the combination of 360° Lidar, Front facing Camera and Corner Facing Radar was found to provide optimal performance in the state estimation and object association for the environment model generated.