Autonomous vehicle perception systems typically rely on single-wavelength lidar sensors to obtain three-dimensional information about the road environment. In contrast to cameras, lidars are unaffected by challenging illumination conditions, such as low light during night-time and various bidirectional effects changing the return reflectance. However, as many commercial lidars operate on a monochromatic basis, the ability to distinguish objects based on material spectral properties is limited. In this work, we describe the prototype hardware for a hyperspectral single photon lidar and demonstrate the feasibility of its use in an autonomous-driving-related object classification task. We also introduce a simple statistical model for estimating the reflectance measurement accuracy of single photon sensitive lidar devices. The single photon receiver frame was used to receive 30 12.3 nm spectral channels in the spectral band 1200–1570 nm, with a maximum channel-wise intensity of 32 photons. A varying number of frames were used to accumulate the signal photon count. Multiple objects covering 10 different categories of road environment, such as car, dry asphalt, gravel road, snowy asphalt, wet asphalt, wall, granite, grass, moss, and spruce tree, were included in the experiments. We test the influence of the number of spectral channels and the number of frames on the classification accuracy with random forest classifier and find that the spectral information increases the classification accuracy in the high-photon flux regime from 50% to 94% with 2 channels and 30 channels, respectively. In the low-photon flux regime, the classification accuracy increases from 30% to 38% with 2 channels and 6 channels, respectively. Additionally, we visualize the data with the t-SNE algorithm and show that the photon shot noise in the single photon sensitive hyperspectral data contributes the most to the separability of material specific spectral signatures. The results of this study provide support for the use of hyperspectral single photon lidar data on more advanced object detection and classification methods, and motivates the development of advanced single photon sensitive hyperspectral lidar devices for use in autonomous vehicles and in robotics.

Hyperspectral LiDAR (HSL) enables the simultaneous acquisition of the surface geometry and spectral signatures of remote natural targets, making it valuable for various applications such as material probing, automated point cloud segmentation, and vegetation health monitoring. We present a first proof-of-concept study of a broadband dual-comb HSL system based on a 1 GHz dual-comb supercontinuum (SC). The SC spans from 820 to 1300 nm, generated via coherent spectral broadening of a free-running single-cavity dual-comb oscillator at 1053 nm in a single nonlinear photonic crystal fiber. The HSL system achieves a sub-µm ranging precision on a non-cooperative target at an update rate of 670 Hz. The shot-noise limited electronic dual-comb interferograms furthermore encode the spectral information of the target reflection across the SC bandwidth. This allows the capture of precise 3D point clouds with spectral signatures, unlocking new possibilities for spectrum-based material classification.