Radar map generation using binary Bayes filter or what is commonly known as Inverse Sensor Model; which translates the sensor measurements into grid cells occupancy estimation, is a classical problem in different fields. In this work, the focus will be on development of Inverse Sensor Model for parking space using 77 GHz FMCW (Frequency Modulated Continuous Wave) automotive radar, that can handle different environment geometrical complexity in a parking space. There are two main types of Inverse Sensor Models, where each has its own assumption about the sensor noise. One that is fixed and is similar to a lookup table, and constructed based on combination of sensor-specific characteristics, experimental data and empirically-determined parameters. The other one is learned by using ground truth labeling of the grid map cell, to capture the desired Inverse Sensor Model. In this work a new Inverse Sensor Model is proposed, that make use of the computational advantage of using fixed Inverse Sensor Model and capturing desired occupancy estimation based on ground truth labeling. A derivation of the occupancy grid mapping problem using binary Bayes filtering would be performed from the well known SLAM (Simultaneous Localization and Mapping) problem, followed by presenting the Adaptive Inverse Sensor Model, that uses fixed occupancy estimation but with adaptive occupancy shape estimation based on statistical analysis of the radar measurements distribution across the acquisition environment. A prestudy of the noise nature of the radar used in this work is performed, to have a common Inverse Sensor Model as a benchmark. Then the drawbacks of such Inverse Sensor Model would be addressed as sub steps of Adaptive Inverse Sensor Model, to be able to haven an optimal grid map occupancy estimator. Finally a comparison between the generated maps using the benchmark and the adaptive Inverse Sensor Model will take place, to show that under the fulfillment of the assumptions of the Adaptive Inverse Sensor Model, the Adaptive Inverse Sensor Model can offer a better visual appealing map to that of the benchmark.