A method comprising: receiving radar data that comprises multiple time-series frames associated with electromagnetic, EM, energy reflected by one or more objects in a roadway and received at an antenna of a radar system attached to a portion of a vehicle, each time-series frame of the radar data including multiple range bins; generating, using the time-series frames of the radar data, a Doppler beam vector of the EM energy; generating, using the Doppler beam vector and a super-resolution operation, a range-azimuth map of the EM energy for each time-series frame of the multiple time-series frames; generating, using the range-azimuth map, an interpolated range-azimuth map of the EM energy by: determining, for each range bin of multiple range bins within the range-azimuth map, a range-bin time, the range-bin time being the time-series frame at which the vehicle reaches the range bin; selecting, for a respective range-bin time, bracketing data captures of the radar data that have a time-series frame immediately before and immediately after the respective range-bin time; determining a range position of the vehicle at the bracketing data captures; and shifting range values of the bracketing data captures in a range dimension so that a location of the vehicle and a stationary object among the one or more objects match those in an interpolated time-series frame; generating, using the interpolated range-azimuth map, a range-time map of the EM energy, the range-time map representing a potential detection of the stationary object among the one or more objects as a straight, 45-degree line; and detecting, using a machine-learned model, stationary objects among the one or more objects, the machine-learned model being configured to receive as inputs extracted features corresponding to the one or more objects from the range-time map for multiple range bins at each of the time-series frames, the extracted features including at least two of range, target angle, velocity, geometrical features, or intensity-based features of the one or more objects. receiving radar data that comprises multiple time-series frames associated with electromagnetic, EM, energy reflected by one or more objects in a roadway and received at an antenna of a radar system attached to a portion of a vehicle, each time-series frame of the radar data including multiple range bins; generating, using the time-series frames of the radar data, a Doppler beam vector of the EM energy; generating, using the Doppler beam vector and a super-resolution operation, a range-azimuth map of the EM energy for each time-series frame of the multiple time-series frames; generating, using the range-azimuth map, an interpolated range-azimuth map of the EM energy by: determining, for each range bin of multiple range bins within the range-azimuth map, a range-bin time, the range-bin time being the time-series frame at which the vehicle reaches the range bin; selecting, for a respective range-bin time, bracketing data captures of the radar data that have a time-series frame immediately before and immediately after the respective range-bin time; determining a range position of the vehicle at the bracketing data captures; and shifting range values of the bracketing data captures in a range dimension so that a location of the vehicle and a stationary object among the one or more objects match those in an interpolated time-series frame; determining, for each range bin of multiple range bins within the range-azimuth map, a range-bin time, the range-bin time being the time-series frame at which the vehicle reaches the range bin; selecting, for a respective range-bin time, bracketing data captures of the radar data that have a time-series frame immediately before and immediately after the respective range-bin time; determining a range position of the vehicle at the bracketing data captures; and shifting range values of the bracketing data captures in a range dimension so that a location of the vehicle and a stationary object among the one or more objects match those in an interpolated time-series frame; generating, using the interpolated range-azimuth map, a range-time map of the EM energy, the range-time map representing a potential detection of the stationary object among the one or more objects as a straight, 45-degree line; and detecting, using a machine-learned model, stationary objects among the one or more objects, the machine-learned model being configured to receive as inputs extracted features corresponding to the one or more objects from the range-time map for multiple range bins at each of the time-series frames, the extracted features including at least two of range, target angle, velocity, geometrical features, or intensity-based features of the one or more objects.