A method comprising: iteratively maintaining (802, 804) at least one set of hypotheses for associations between radar tracks and vision tracks within a sliding window (732, 734, 742, 744) that includes at least two consecutive scans of both radar data for an environment of a vehicle obtained using one or more radar sensors on the vehicle and vision data for the environment of the vehicle obtained using one or more vision sensors on the vehicle for each of the at least two consecutive scans included in the sliding window (732, 734, 742, 744), obtaining the radar tracks and the vision tracks based on the radar data from the one or more radar sensors on the vehicle and the vision data from the one or more vision sensors on the vehicle, respectively, the one or more radar sensors having a first field of view that at least partially overlaps with a second field of view of the one or more vision sensors, maintaining, based on the at least two consecutive scans of each of the radar data and the vision data, at least one set of hypotheses for associations between the vision tracks and the radar tracks, the at least one set of hypotheses including mass values for the associations between the vision tracks and the radar tracks within the sliding window (732, 734, 742, 744), and sliding the sliding window (732, 734, 742, 744) such that a first one of the at least two consecutive scans is removed from the sliding window and a next scan consecutively after the two consecutive scans is added to the sliding window; determining, based on the mass values, a probability value for each hypothesis of the at least one set of hypotheses within the sliding window (732, 734, 742, 744), the probability value indicating a likelihood that each hypothesis is an accurate association between the vision tracks and the radar tracks; determining, based on the probability value for each hypothesis, one or more matches between one or more radar tracks and a vision track, the one or more matches comprising one or more hypotheses with probability values that exceed a decision threshold; and outputting, to a semi-autonomous or autonomous driving system of the vehicle, the one or more matches to control operation of the vehicle. iteratively maintaining (802, 804) at least one set of hypotheses for associations between radar tracks and vision tracks within a sliding window (732, 734, 742, 744) that includes at least two consecutive scans of both radar data for an environment of a vehicle obtained using one or more radar sensors on the vehicle and vision data for the environment of the vehicle obtained using one or more vision sensors on the vehicle for each of the at least two consecutive scans included in the sliding window (732, 734, 742, 744), obtaining the radar tracks and the vision tracks based on the radar data from the one or more radar sensors on the vehicle and the vision data from the one or more vision sensors on the vehicle, respectively, the one or more radar sensors having a first field of view that at least partially overlaps with a second field of view of the one or more vision sensors, maintaining, based on the at least two consecutive scans of each of the radar data and the vision data, at least one set of hypotheses for associations between the vision tracks and the radar tracks, the at least one set of hypotheses including mass values for the associations between the vision tracks and the radar tracks within the sliding window (732, 734, 742, 744), and sliding the sliding window (732, 734, 742, 744) such that a first one of the at least two consecutive scans is removed from the sliding window and a next scan consecutively after the two consecutive scans is added to the sliding window; obtaining the radar tracks and the vision tracks based on the radar data from the one or more radar sensors on the vehicle and the vision data from the one or more vision sensors on the vehicle, respectively, the one or more radar sensors having a first field of view that at least partially overlaps with a second field of view of the one or more vision sensors, maintaining, based on the at least two consecutive scans of each of the radar data and the vision data, at least one set of hypotheses for associations between the vision tracks and the radar tracks, the at least one set of hypotheses including mass values for the associations between the vision tracks and the radar tracks within the sliding window (732, 734, 742, 744), and sliding the sliding window (732, 734, 742, 744) such that a first one of the at least two consecutive scans is removed from the sliding window and a next scan consecutively after the two consecutive scans is added to the sliding window; determining, based on the mass values, a probability value for each hypothesis of the at least one set of hypotheses within the sliding window (732, 734, 742, 744), the probability value indicating a likelihood that each hypothesis is an accurate association between the vision tracks and the radar tracks; determining, based on the probability value for each hypothesis, one or more matches between one or more radar tracks and a vision track, the one or more matches comprising one or more hypotheses with probability values that exceed a decision threshold; and outputting, to a semi-autonomous or autonomous driving system of the vehicle, the one or more matches to control operation of the vehicle.