In this study, a light detection and ranging system (LIDAR) was designed that codes pixel location information in its laser pulses using the direct- sequence optical code division multiple access (DS-OCDMA) method in conjunction with a scanning-based microelectromechanical system (MEMS) mirror. This LIDAR can constantly measure the distance without idle listening time for the return of reflected waves because its laser pulses include pixel location information encoded by applying the DS-OCDMA. Therefore, this emits in each bearing direction without waiting for the reflected wave to return. The MEMS mirror is used to deflect and steer the coded laser pulses in the desired bearing direction. The receiver digitizes the received reflected pulses using a low-temperature-grown (LTG) indium gallium arsenide (InGaAs) based photoconductive antenna (PCA) and the time-to-digital converter (TDC) and demodulates them using the DS-OCDMA. When all of the reflected waves corresponding to the pixels forming a range image are received, the proposed LIDAR generates a point cloud based on the time-of-flight (ToF) of each reflected wave. The results of simulations performed on the proposed LIDAR are compared with simulations of existing LIDARs.

LIDAR scanners are essential components of intelligent vehicles capable of autonomous travel. Mutual interference between LIDAR scanners has not been regarded as a problem yet. Mutual interference was identified as a problem of increased importance because of the appearance of safety functions and the increasing rate of vehicles equipped with LIDAR scanner. This paper will show the probability that any LIDAR scanner is probably interfered mutually by considering spatial and temporal overlaps. It will present three types of experiments and their results are showed, according to the arrangement of two LIDAR scanners.

LIDAR scanners are essential components of intelligent vehicles capable of autonomous travel. Mutual interference between LIDAR scanners has not been regarded as a problem yet. Mutual interference was identified as a problem of increased importance because of the appearance of safety functions and the increasing rate of vehicles equipped with LIDAR scanner. This paper will show the probability that any LIDAR scanner will interfere mutually by considering spatial and temporal overlaps. It will present some generic interference scenarios and report on the current status of the analysis of interference mechanisms.

Recent advancements in computer vision technology, developments in sensors and sensor-collecting approaches, and the use of deep and transfer learning approaches have excelled in the development of autonomous vehicles. On-road vehicle detection has become a task of significant importance, especially due to exponentially increasing research on autonomous vehicles during the past few years. With high-end computing resources, a large number of deep learning models have been trained and tested for on-road vehicle detection recently. Vehicle detection may become a challenging process especially due to varying light and weather conditions like night, snow, sand, rain, foggy conditions, etc. In addition, vehicle detection should be fast enough to work in real time. This study investigates the use of the recent YOLO version, YOLOx, to detect vehicles in bad weather conditions including rain, fog, snow, and sandstorms. The model is tested on the publicly available benchmark dataset DAWN containing images containing four bad weather conditions, different illuminations, background, and number of vehicles in a frame. The efficacy of the model is evaluated in terms of precision, recall, and mAP. The results exhibit the better performance of YOLOx-s over YOLOx-m and YOLOx-l variants. YOLOx-s has 0.8983 and 0.8656 mAP for snow and sandstorms, respectively, while its mAP for rain and fog is 0.9509 and 0.9524, respectively. The performance of models is better for snow and foggy weather than rainy weather sandstorms. Further experiments indicate that enhancing image quality using multiscale retinex improves YOLOx performance.

We proposed a light detection and ranging (LIDAR) system that changes the measurement strategy from a LIDAR system of sequential emission and measuring method to a concurrent firing measuring method. The proposed LIDAR was a 3D scanning LIDAR method that consisted of 128 output channels in one vertical line in the measurement direction and concurrently measured the distance for each of these 128 channels. The scanning LIDAR emitted 128 laser pulse streams encoded by carrier-hopping prime code (CHPC) technology with identification and checksum. When the reflected pulse stream was received and demodulated, the emission channel could be recognized. This information could be used to estimate the time when the laser pulse stream was emitted and calculate the distance to the object reflecting the laser. By using the identification of the received reflected wave, even if several positions were measured at the same time, the measurement position could be recognized after the reception. Extensive simulations indicated that the proposed LIDAR could provide autonomous vehicles or autonomous walking robots with good distance images to recognize the environment ahead.

In the coded pulse scanning light detection and ranging (LIDAR) system, the number of laser pulses used at a given measurement point changes depending on the modulation and the method of spreading used in optical code-division multiple access (OCDMA). The number of laser pulses determines the pulse width, output power, and duration of the pulse transmission of a measurement point. These parameters determine the maximum measurement distance of the LIDAR and the number of measurement points that can be employed per second. In this paper, we suggest possible combinations of modulation and spreading technology that can be used for OCDMA, evaluate their performance and characteristics, and study optimal combinations according to varying operating environments.