## 7 Conclusions  
Our article has provided the first-ever results of a systematic and comprehensive study of how radar micro-Doppler data can be used to infer the gear engagement and occupancy levels of four different types of vehicles. In addition to vehicle START, gear engagements included PARK, REVERSE, NEUTRAL, and DRIVE. Occupancy levels varied from one to four persons while in PARK. Vehicles investigated included a sedan, a hatchback, a compact SUV, and an SUV. Radar data were collected from all four sides of the vehicle contemporaneously with accelerometer data for comparison and validation. Our results indicate that there are noticeable differences in the micro-Doppler spectrograms and statistics derived from the power spectral densities between gear engagements since these can be related to the engine functionality. However, the results for the occupancy levels were inconclusive since the weight of humans does not alter the overall weight of the occupied vehicle in a significant manner. The spectrograms reveal distinct tonal vibrations, primarily around 30 Hz, originating from the engine's combustion frequency during idling, with harmonics observed at 60 Hz and 90 Hz. Low-frequency vibrations up to ~5 Hz are also present, particularly on the vehicle's rear side due to surface geometry and radar beam smearing. Both radar and accelerometer spectrograms consistently capture these frequency patterns, confirming their engine-based origin, though occasional unexplained tones like 40 Hz also appear. Passenger occupancy has minimal impact on vibration transmission, as the vehicle's large mass dominates structural response regardless of load. The PRND spectrograms show distinct low-frequency impulsive excitation patterns around each 10 s mark, corresponding to gear shifts, with most activity in the 0-10 Hz range and extending up to 20 Hz shortly after shifting. Spectral lines at 30 Hz and 60 Hz persist throughout, with amplitude variations likely caused by slight engine RPM fluctuations during gear changes. Despite consistent testing, results varied between sides of the vehicle, possibly due to a limited number of recordings, though gear shift signatures were still detectable from all sides. These findings suggest that spectrogram analysis could be useful for training machine learning models to identify transmission modes based on impulse and spectral features. The comparison of averaged Power Spectral Densities (PSDs) from radar and accelerometer data shows strong agreement, especially around the 30 Hz range, highlighting consistent vehicle vibration patterns likely driven by engine activity. Normalization enabled meaningful comparison between the two sensor types, revealing shared tonal components across test conditions and confirming that certain vehicle regions, like the hood, are more vibration prone. While PSD analysis of steady-state gear modes showed only minor frequency amplitude differences, these may be insufficient for classification by machine learning. However, analysis of unaveraged transient data from gear shifts revealed distinct frequency peaks--including at 50, 80, and 100 Hz--suggesting that short-duration transients could provide clearer signatures for identifying specific gear transitions. The analysis identifies 23 Hz and 45 Hz as key frequency features for characterizing vehicle states, such as whether the car is ON or OFF and which gear it is in. For the 2015 Kia Forte5, the 23 Hz feature appears only when the engine is on, with power increasing significantly across all gears and peaking in DRIVE mode. The 45 Hz feature, on the other hand, is primarily present when the vehicle is in REVERSE or DRIVE, indicating it can be used to detect motion-ready gear modes. Power distributions at these frequencies differ clearly between gear states in both radar and acoustic data, with DRIVE consistently showing the highest returned power levels. Radar data, though weaker than acoustic due to path loss, still mirrors the same trends and offers effective remote sensing capability.