Light detection and ranging (LiDAR) sensors help autonomous vehicles detect the surrounding environment and the exact distance to an object’s position. Conventional LiDAR sensors require a certain amount of power consumption because they detect objects by transmitting lasers at a regular interval according to a horizontal angular resolution (HAR). However, because the LiDAR sensors, which continuously consume power inefficiently, have a fatal effect on autonomous and electric vehicles using battery power, power consumption efficiency needs to be improved. In this paper, we propose algorithms to improve the inefficient power consumption of conventional LiDAR sensors, and efficiently reduce power consumption in two ways: (a) controlling the HAR to vary the laser transmission period ($T_P$) of a laser diode (LD) depending on the vehicle’s speed and (b) reducing the static power consumption using a sleep mode, depending on the surrounding environment. The proposed LiDAR sensor with the HAR control algorithm reduces the power consumption of the LD by 6.92% to 32.43% depending on the vehicle’s speed, compared to the maximum number of laser transmissions ($N_{x.max}$). The sleep mode with a surrounding environment-sensing algorithm reduces the power consumption by 61.09%. The algorithm of the proposed LiDAR sensor was tested on a commercial processor chip, and the integrated processor was designed as an IC using the Global Foundries 55 nm CMOS process.

Autonomous driving vehicle such as car or auto-mated guided vehicle (AGV), requires sensors to detect obstacles in its surrounding environment and feed the information back to the vehicle control system. Light detection and ranging (LiDAR) sensors, which have attracted attention as effective sensors for autonomous driving, provide distance values measured within field of view using a laser to generate three-dimensional (3D) coordinates. 3D coordinates have the advantage of providing more precise measurements of an object’s shape than other sensors and can also indicate the distance to the object. However, as the amount of data generated for a 3D point cloud and the number of sensors increases, the total amount of data to be processed increases. Therefore, the processing of this raw data in the main processor of an autonomous vehicle becomes a huge burden. In order to reduce the burden on the main processor of the autonomous vehicle, this paper intends to develop a real-time embedded system that primarily processes data from LiDAR sensors.

As the information technology (IT) industry advances, embedded systems are being applied in various industrial sectors. With the expansion of application areas, there is a growing demand for high-precision, high-specification embedded systems, leading to the increased complexity of embedded software. Consequently, software errors can cause system malfunctions, resulting in accidents such as airplane crashes and the sudden acceleration of cars, leading to significant loss of life and property damage. Therefore, measures to ensure the safety and stability of increasing embedded systems malfunctions are necessary. This paper proposes a system that monitors the operation of target embedded systems in real-time and compares the extracted normal operation current/voltage patterns with the current/voltage data of a target embedded system (TES). It compares the operation data of the TES with automatically generated normal operation patterns by forcibly exposing them. It suggests algorithms for immediately detecting and efficiently recovering from the TES malfunctions. The proposed system applies two algorithms. (a) Monitoring TES current: When a malfunction is detected, a monitoring embedded systme (MES) resets the TES to restore normal operation. If malfunctions persist, it controls TES by using an algorithm to shut it down. Additionally, a proportional integral derivation (PID) control is applied to stabilize the current state. (b) Monitoring TES voltage: If a voltage drop occurs, the MES immediately stops the TES operation to minimize damage. The proposed algorithms were validated through experiments. For a normal TES consuming up to 95 mA, an error detection rate of 20% was applied. The TES was reset if it consumed over 114 mA. It was confirmed that the TES was stopped upon detecting the third malfunction. Regarding voltage, when the normal operating voltage of the system was around 5 V, if the TES operating voltage dropped below 4.3 V, it was detected as a malfunction, and the algorithm to stop the TES operation was validated.

This paper experimentally analyzes how stepwise platform vibration (Baseline-S3, approximately 0.3–0.6 mm amplitude) alters the statistical structure of distance measurement errors in a dual-channel LIght Detection And Ranging (LiDAR) (0° and −3°) at a fixed horizontal distance of 1.5 m. The mean error remained at the ${10}^{-5}$ m level across all vibration stages, indicating negligible systematic bias. However, distribution-based metrics showed substantial amplification. The interquartile range (IQR) increased by approximately threefold from Baseline to S3, while the total error range expanded by roughly 4–11 times. The outlier ratio increased by about 1.5–2 times under high-vibration conditions. Both variance and root mean square error (RMSE) exhibited nonlinear growth with increasing vibration intensity. Two-way analysis of variance (ANOVA) revealed no statistically significant differences at the mean level ($p>0.05$), whereas variability-based indicators consistently demonstrated dispersion amplification. These findings indicate that LiDAR degradation under vibration is governed primarily by stochastic dispersion expansion and extreme-value behavior rather than systematic bias shift.

Algorithm of Modified Single-slope A/D Converter with Improved Conversion Time for CMOS Image Sensor System Single-slope A/D converter;Conversion time;Pipeline A/D converter;A/D converter;CMOS image sensor; This paper proposes an algorithm that reduces the conversion time of a single-slope A/D converter (SSADC) that has n-bit resolution, which typically is limited by conversion time taking up to $2^n$ clock cycles for an operation. To improve this situation, we have researched a novel hybrid-type A/D converter that consists of a pseudo-pipeline A/D converter and a conventional SSADC. The pseudo-pipeline A/D converter, using a single-stage of analog components, determines the most significant bits (MSBs) or upper bits and the conventional SSADC determines the remaining bits. Therefore, the modified SSADC, similar to the hybrid-type A/D converter, is able to significantly reduce the conversion time because the pseudo-pipeline A/D converter, which determines the MSBs (or upper bits), does not rely on a clock. The proposed A/D converter was designed using a $0.35-{\mu}m$ 2-poly 4-metal standard complementary metal oxide semiconductor (CMOS) technology process; additionally, its characteristics were simulated.

Fixed angular separation between LiDAR (Light Detection and Ranging) channels results in different path lengths when the channels observe the same planar target; therefore, geometric effects must be considered when comparing inter-channel range measurements. This study investigated inter-channel range differences in a 2-channel LiDAR with fixed 0° and −3° viewing directions, focusing on short-term temporal dependence and temporal averaging. Repeated static measurements were conducted at 1∼5 m with five independent runs per distance. After the path-length difference expected from the nominal channel geometry was removed as a preprocessing step, the mean inter-channel residual ranged from −0.201 to 1.731 mm. The residual showed short-term positive temporal dependence, with a lag-1 autocorrelation of approximately 0.5 that attenuated toward zero within approximately 0.2∼0.3 s. Temporal averaging reduced residual variability more slowly than the $1/\sqrt{N}$ expectation for independent observations, while an autoregressive model of order 1 (AR(1))-based reference closely reproduced the empirical averaging behavior. These results demonstrate that short-term temporal dependence should be considered when evaluating precision improvement through temporal averaging of repeated inter-channel LiDAR measurements.