Table 1.

Overview of the state-of-the-art LiDAR sensor model working principles and validation approaches.

Authors	Model Type	Input of Model	Output of Model	Covered Effects	Validation Approach
Hanke et al. [23]	Ideal/low-fidelity	Object list	Object list	FoV and object occlusion	N/A
Stolz & Nestlinger [24]	Ideal/low-fidelity	Object list	Object list	FoV and object occlusion	N/A
Muckenhuber et al. [26]	Phenomenological/ low-fidelity	Object list	Object list	FoV, object class definition, occlusion, probability of false positive and false negative detections	Simulation result
Linnhoff et al. [27]	Phenomenological/ low-fidelity	Object list	Object list	Partial occlusion of objects, limitation of angular view, and  decrease in the effective range due to atmospheric attenuation	Simulation result comparison with ray tracing model at object level
Hirsenkorn et al. [25]	Phenomenological/ low-fidelity	Object list	Object list	Ranging errors, latency, false-positive, and occlusion	Simulation result
Zhao et al. [28]	Phenomenological/ low-fidelity	Object list	Object list or point clouds	Occlusion, FoV and beam divergence	Simulation result
Li et al. [29]	Physical/ medium-fidelity	Object list	Object list or point clouds	Occlusion, FoV and beam divergence	Simulation result
Philipp et al. [31]	Physical/ medium-fidelity	Ray-casting	Point clouds & object list	Beam divergence, SNR, detection threshold, and material surface properties	Qualitative compar- ison with real and re- ference measuremen- ts at the object list le- vel for one dynamic scenario
Gschwandtner  et al. [32]	Physical/ medium-fidelity	Ray-casting	Point clouds	Sensor noise, materials physical properties, and FSPL	Simulation results
Goodin et al. [33]	Physical/ medium-fidelity	Ray-casting	Point clouds	Beam divergence and a Gaussian beam profile	Simulation results
Bechtold & Höfle [34]	Physical/ medium-fidelity	Ray-casting	Point clouds	Beam divergence, atmospheric attenuation, scanner efficiency, and material surface properties	Simulation results
Hanke et al. [35]	Physical/ medium-fidelity	Ray-tracing	Point clouds	Beam divergence, material surface properties, detection threshold, noise effects, and atmospheric attenuation	Qualitative comparis- on of synthetic and re- al data at point cloud level for one dynamic scenario
Li et al. [29]	Physical/ medium-fidelity	Ray-tracing	Point clouds	Beam divergence, power loss due to rain, fog, snow, and haze	Simulation results for one static and one dy- namic scenario
Zhao et al. [28]	Physical/ medium-fidelity	Ray-tracing	Point clouds	False alarm due to the backscattering from water droplets	Qualitative comparis- on with measurement
CARLA [37]	Physical/ medium-fidelity	Ray-casting	Point clouds	signal attenuation, noise the drop-off in number of point clouds loss due to external perturbations	N/A
CarMaker [20]	Physical/ medium-fidelity	Ray-tracing	Point clouds	Noise, the drop-off in intensity, and the number of point clouds due to atmospheric attenuation	N/A
DYNA4 [38]	Physical/ medium-fidelity	Ray-casting	Point clouds	Physical effects, the material surface reflectivity and ray angle of incidence	N/A
VTD [40]	Physical/ medium-fidelity	Ray-tracing	Point clouds	Material properties	N/A
AURELION [39]	Physical/ medium-fidelity	Ray-tracing	Point clouds	Material surface reflectivity, sensor noise, atmospheric attenuation, and fast motion scan effect	N/A
Haider et al. (proposed model)	Physical/ high-fidelity	Ray-tracing	Time domain & point clouds	Material surface reflectivity, beam divergence, FSPL daylight, daylight filter, internal reflection of detector saturation of detector from bright targets, detector shot noise and dark count rate, and detection threshold	Qualitative comparison of simulation and real measurement at time do- main and point cloud level