With the advent of new generation information technologies in industry and product design, the big data-driven product design era has arrived. However, the big data-driven product design mainly pla...

Digital twin-driven product design framework

Autonomous vehicles rely on their perception systems to acquire information about their immediate surroundings. It is necessary to detect the presence of other vehicles, pedestrians, and other relevant entities. Safety concerns and the need for accurate estimations have led to the introduction of lidar systems to complement camera- or radar-based perception systems. This article presents a review of state-of-the-art automotive lidar technologies and the perception algorithms used with those technologies. Lidar systems are introduced first by analyzing such a system?s main components, from laser transmitter to beamscanning mechanism. The advantages/disadvantages and the current status of various solutions are introduced and compared. Then, the specific perception pipeline for lidar data processing is detailed from an autonomous vehicle perspective. The model-driven approaches and emerging deep learning (DL) solutions are reviewed. Finally, we provide an overview of the limitations, challenges, and trends for automotive lidars and perception systems.

Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems

With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Lidar imaging systems are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a race dedicated to deciding the final solution to be implemented. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. Furthermore, the performance data of each approach often arise from different manufacturers and developers, which usually have some interest in the dispute. Within this paper, we intend to overcome the situation by providing an introductory, neutral overview of the technology linked to lidar imaging systems for autonomous vehicles, and its current state of development. We start with the main single-point measurement principles utilized, which then are combined with different imaging strategies, also described in the paper. An overview of the features of the light sources and photodetectors specific to lidar imaging systems most frequently used in practice is also presented. Finally, a brief section on pending issues for lidar development in autonomous vehicles has been included, in order to present some of the problems which still need to be solved before implementation may be considered as final. The reader is provided with a detailed bibliography containing both relevant books and state-of-the-art papers for further progress in the subject.

An Overview of Lidar Imaging Systems for Autonomous Vehicles

In this paper, we are presenting a short overview of the sensors and sensor fusion in autonomous vehicles. We focused on the sensor fusion from the key sensors in autonomous vehicles: camera, radar, and lidar. The current state-of-the-art in this area will be presented, such as 3D object detection method for leveraging both image and 3D point cloud information, moving object detection and tracking system, and occupancy grid mapping used for navigation and localization in dynamic environments. It is shown that including more sensors into sensor fusion system benefits with better performance and the robustness of the solution. Moreover, usage of camera data in localization and mapping, that is traditionally solved by radar and lidar data, improves the perceived model of the environment. Sensor fusion has a crucial role in autonomous systems overall, therefore this is one of the fastest developing areas in the autonomous vehicles domain.

Sensors and Sensor Fusion in Autonomous Vehicles

This article focuses on testing and investigating further development needs for LiDARs in self-driving cars in adverse weather. The article compares two different LiDARs (Ibeo Lux and Velodyne PUCK), which both use the 905 nm wavelengths, which are used in more than 95% of currently available LiDARs. The performance was tested and estimated in stabilized fog conditions at Cerema fog chamber facilities. This provides a good basis for repeating the same validation procedure multiple times and ensuring the right development decisions. However, performance of the LiDARs suffers when the weather conditions become adverse and visibility range decreases. A 50% reduction in target detection performance was observed over the exhaustive tests. Therefore, changing to higher wavelengths (1550 nm) was considered using redesigned “pre-prototype LiDAR”. The preliminary results indicate that there is no reason to not use 1550 nm wavelength, which due to eye safety regulations gives an opportunity to use 20 times more power compared to the traditional 905 nm. In order to clarify the expected benefits, additional feasibility studies are still needed.

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Automotive LiDAR performance verification in fog and rain

This article focuses on development baseline for a novel LIDAR for future autonomous cars, which require perception not only in clear weather, but also under harsh weather conditions such as fog and rain. Development of automotive laser scanners is bound to the following requirements: maximize sensor performance, assess the performance level and keep the scanner component costs reasonable (<1000 €) even if more expensive optical and electronic components are needed. The objective of this article is to review the existing automotive laser scanners and their capabilities to pave the way for developing new scanner prototypes, which are more capable in harsh weather conditions. Testing of scanner capabilities has been conducted in the northern part of the Finland, at Sodankyla Airport, where fog creates a special problem. The scanner has been installed in the airport area for data gathering and analyzes if fog, snow or rain are visible in the scanner data. The results indicate that these conditions degrade sensor performance by 25%, and therefore, future work in software module development should take this into account with in-vehicle system performance estimations concerning the visual range of the scanner. This allows the vehicle to adapt speed, braking distance and stability control systems accordingly.

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Automotive LIDAR sensor development scenarios for harsh weather conditions

Light detection and ranging sensors (LiDARS) are the most promising devices for range sensing in automated cars and therefore, have been under intensive development for the last five years. Even though various types of resolutions and scanning principles have been proposed, adverse weather conditions are still challenging for optical sensing principles. This paper investigates proposed methods in the literature and adopts a common validation method to perform both indoor and outdoor tests to examine how fog and snow affect performances of different LiDARs. As suspected, the performance degraded with all tested sensors, but their behavior was not identical.

/pdf/testing-and-validation-of-automotive-point-cloud-sensors-in-1gj1msotw0.pdf

Testing and validation of automotive point-cloud sensors in adverse weather conditions

This paper suggests a new schema for applying the IoT (Internet of Things) to intelligent traffic systems. The intelligent traffic system is implemented using road side units (RSU) with friction monitoring, vehicles with environmental sensors and a database for data transfer through different platforms. The system is able to collect sensor data from stationary RSU stations or moving vehicles and store it to the database. The test results indicate that the developed IcOR friction monitoring unit is able to distinguish the different road weather categories (ice, snow, wet and dry asphalt) with sufficient accuracy. Communication is implemented using a V2I/I2V IEEE 802.11p communication between RSUs and vehicles or 3G/4G mobile connections. In this article, we describe an implemented IoT ITS concept with current real-life implementation and future plans.

IoT for intelligent traffic system

This article studies the smoke and exhaust detection system that has been developed for monitoring exhaust gases to enforce environmental laws and regulations. In many highly populated countries the HSU (Hart ridge Smoke Unit) grade is used to impose penalties. In many cases, HSU values over 40 … 50 are leading to legal actions.

Pasi Pyykönen

Papers

Automotive LiDAR performance verification in fog and rain

Automotive LIDAR sensor development scenarios for harsh weather conditions

Testing and validation of automotive point-cloud sensors in adverse weather conditions

IoT for intelligent traffic system

Multi-camera-based smoke detection and traffic pollution analysis system