Abstract: Systems and techniques of the present disclosure may access data from a time-of-flight (TOF) sensor of an autonomous vehicle (AV). The TOF sensor may have light signals and received reflections of those transmitted signals such that a set of simulation data can be generated. This set of simulation data may identify a distance to associate with an object that is different from a calibration distance. Equations may be used to identify a light signal amplitude, a signal to noise ratio (SNR), and a range inaccuracy due to noise from the accessed data. The identified the light signal amplitude, the SNR, and the range inaccuracy due to noise may have been identified using equations. Once the set of simulation data is generated, it may be saved for later access by a processor executing a simulation program used to train devices used to control the driving of an AV.

Abstract: A combination of lookup tables for waveforms and several scaling and delay factors to accurately simulate touch and collision transducer sensor output without the complexity of solving the classical wave equation or performing convolutions in the frequency domain. TACT, as used herein, is a touch and collision transducer sensor used for detecting collisions. When the vehicle collides with an object a wave will be generated by the touch and collision transducer sensors. The waveform can take many forms depending on various parameter. Pre-recorded waveforms can be used to simulate the collisions and produce appropriate outputs. However, the present disclosure contemplates more complex, diverse waveforms which can be produced based on the existing pre-recorded libraries. To this end, simulated waveforms can be produced by scaling, filtering, delaying and combining with road noise, based on the impact material and contact area, at least in part.

Abstract: Techniques for simulating LIDAR data are described. In one embodiment, a method for simulating LIDAR data may include retrieving a simulated scene that simulates a real-world scene, the simulated scene including at least one target object having a reflectivity r and located at a range R from a LIDAR sensor, the LIDAR sensor having at least one intrinsic parameter; generating a probability of detection (Pd) drop-off function for the LIDAR sensor, wherein the Pd drop-off function is related to r, R, and the at least one intrinsic parameter; for each data point comprising a ray emitted by the LIDAR sensor that hits the target object, generating a Pd value using the Pd drop-off function; and determining based on the Pd value whether to drop the data point.