Assessment of Different Sensor Configurations for Collaborative Driving in Urban Environments

Abstract EN

Vehicle-to-vehicle relative navigation of a network of vehicles travelling in an urban canyon is assessed using least-squares and Kalman filtering covariance simulation techniques.

Between-vehicle differential GPS is compared with differential GPS augmented with between-vehicle ultrawideband range and bearing measurements.

The three measurement types are combined using both least-squares and Kalman filtering to estimate the horizontal positions of a network of vehicles travelling in the same direction on a road in a simulated urban canyon.

The number of vehicles participating in the network is varied between two and nine while the severity of the urban canyon was varied from 15-to 65-degree elevation mask angles.

The effect of each vehicle’s azimuth being known a priori, or unknown is assessed.

The resulting relative positions in the network of vehicles are then analysed in terms of horizontal accuracy and statistical reliability of the solution.

The addition of both range and bearing measurements provides protection levels on the order of 2 m at almost all times where DGPS alone only rarely has observation redundancy and often exhibits estimated accuracies worse than 200 m.

Reliability is further improved when the vehicle azimuth is assumed to be known a priori.