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The conventional radar modeling methods for automotive applications were either function-based or physics-based. The former approach was mainly abstracted as a solution of the intersection between geometric representations of radar beam and targets, while the latter one took radar detection mechanism into consideration by means of “ray tracing”. Although they each has its unique advantages, they were often unrealistic or time-consuming to meet actual simulation requirements. This paper presents a combined geometric and physical modeling method on millimeter-wave radar systems for Frequency Modulated Continuous Wave (FMCW) modulation format under a 3D simulation environment. With the geometric approach, a link between the virtual radar and 3D environment is established. With the physical approach, on the other hand, the ideal target detection and measurement are contaminated with noise and clutters aimed to produce the signals as close to the real ones as possible.

Aimed to provide an effective solution for control-oriented applications, this paper proposes a novel method using a high-precision digital map to achieve high-accuracy positioning with fast updating rate. First, the map is developed using a high-definition LiDAR (Velodyne HDL 64E) and a RTK-GNSS system, which contains lane-level waypoints, road width, curb and typical obstacles along the road. Next, a robust version of ICP (Iterative Closest Point) is proposed to clean the corresponding points of large errors on map matching (MM). Finally, based on the large set of data from the environmental map, an unscented Kalman filter (UKF) is applied to fuse GNSS signal and dead reckoning (DR) to estimate the position. Thus the searching scope on the map can be considerably reduced so that the matching speed can be greatly improved. The high-precision digital map can be used not only for global path planning, but also for local driving detection and path planning.

Environmental sensing and perception is one of the key technologies on intelligent driving or autonomous vehicles. As a complementary part to current radar and lidar sensors, ultrasonic sensor has become more and more popular due to its high value to the cost. Different from other sensors mainly based on propagation of electromagnetic wave, ultrasonic sensor possesses some unique features and physical characteristics that bring many merits to autonomous vehicle research, like transparent obstacles and highly reflective surfaces detection. Its low-cost property can further bring down hardware cost to foster widespread use of intelligent driving or autonomous vehicles. To accelerate the development of autonomous vehicle, this paper proposes a high fidelity ultrasonic sensor model based on its physical characteristics, including obstacle detection, distance measurement and signal attenuation.