Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors

Automatic Stub Avoidance for a Powered Prosthetic Leg Over Stairs and Obstacles

Passive prosthetic legs require undesirable compensations from amputee users to avoid stubbing obstacles and stairsteps. Powered prostheses can reduce those compensations by restoring normative joint biomechanics, but the absence of user proprioception and volitional control combined with the absence of environmental awareness by the prosthesis increases the risk of collisions. This article presents a novel stub avoidance controller that automatically adjusts prosthetic knee/ankle kinematics based on suprasensory measurements of environmental distance from a small, lightweight, low-power, low-cost ultrasonic sensor mounted above the prosthetic ankle. In a case study with two transfemoral amputee participants, this control method reduced the stub rate during stair ascent by 89.95% and demonstrated an 87.50% avoidance rate for crossing different obstacles on level ground. No thigh kinematic compensation was required to achieve these results. These findings demonstrate a practical perception solution for powered prostheses to avoid collisions with stairs and obstacles while restoring normative biomechanics during daily activities.

Runtime unknown unsafe scenarios identification for SOTIF of autonomous vehicles

Safety is a critical concern for autonomous vehicles (AVs). Current testing approaches face challenges in simultaneously meeting the requirements of being valid, safe, and fast. To address these challenges, the silent testing approach that tests functions or systems in the background without interfering with driving is motivated. Building upon our previous research, this study first extends the method to specifically address the validation of AV perception, utilizing a lane marking detection algorithm (LMDA) as a case study. Second, field experiments were conducted to investigate the method's effectiveness in validating AV systems. For both studies, an architecture for describing the working principle is presented. The efficacy of the method in evaluating the LMDA is demonstrated through the use of adversarial images generated from a dataset. Furthermore, various scenarios involving pedestrians crossing a road under different levels of criticality were constructed to gain practical insights into the method's applicability for AV system validation. The results show that corner cases of the LMDA are successfully identified by the given evaluation metrics. Furthermore, the experiments highlight the benefits of employing multiple virtual instances with different initial states, enabling the expansion of the test space and the discovery of unknown unsafe scenarios, particularly those prone to false-positive objects. The practical implementation and systematic discussion of the method offer a significant contribution to AV safety validation.

Performance Verification of Autonomous Driving LiDAR Sensors under Rainfall Conditions in Darkroom

This research aims to assess the functionality of the VLP-32 LiDAR sensor, which serves as the principal sensor for object recognition in autonomous vehicles. The evaluation is conducted by simulating edge conditions the sensor might encounter in a controlled darkroom setting. Parameters for environmental conditions under examination encompass measurement distances ranging from 10 to 30 m, varying rainfall intensities (0, 20, 30, 40 mm/h), and different observation angles (0°, 30°, 60°). For the material aspects, the investigation incorporates reference materials, traffic signs, and road surfaces. Employing this diverse set of conditions, the study quantitatively assesses two critical performance metrics of LiDAR: intensity and NPC (number of point clouds). The results indicate a general decline in intensity as the measurement distance, rainfall intensity, and observation angles increase. Instances were identified where the sensor failed to record intensity for materials with low reflective properties. Concerning NPC, both the effective measurement area and recorded values demonstrated a decreasing trend with enlarging measurement distance and angles of observation. However, NPC metrics remained stable despite fluctuations in rainfall intensity.

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle's vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors.

Camera-LiDAR Fusion Method with Feature Switch Layer for Object Detection Networks

Object detection is an important factor in the autonomous driving industry. Object detection for autonomous vehicles requires robust results, because various situations and environments must be considered. A sensor fusion method is used to implement robust object detection. A sensor fusion method using a network should effectively meld two features, otherwise, there is concern that the performance is substantially degraded. To effectively use sensors in autonomous vehicles, data analysis is required. We investigated papers in which the camera and LiDAR data change for effective fusion. We propose a feature switch layer for a sensor fusion network for object detection in cameras and LiDAR. Object detection performance was improved by designing a feature switch layer that can consider its environment during network feature fusion. The feature switch layer extracts and fuses features while considering the environment in which the sensor data changes less than during the learning network. We conducted an evaluation experiment using the Dense Dataset and confirmed that the proposed method improves the object detection performance.