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.

Automotive perception involves understanding the external driving environment as well as the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This paper provides an overview of different sensor modalities like cameras, radars, and LiDARs used commonly for perception, along with the associated data processing techniques. Critical aspects in perception are considered, like architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.

By transmitting lasers and processing laser returns, LiDAR (light detection and ranging) perceives the surrounding environment through distance measurements. Because of high ranging accuracy, LiDAR is one of the most critical sensors in autonomous driving systems. Revolving around the 3D point clouds generated from LiDARs, plentiful algorithms have been developed for object detection/tracking, environmental mapping, or localization. However, a LiDAR's ranging performance suffers under adverse weather (e.g. fog, rain, snow etc.), which impedes full autonomous driving in all weather conditions. This article focuses on analyzing the performance of a typical time-of-flight (ToF) LiDAR under fog environment. By controlling the fog density within CEREMA Adverse Weather Facility, the relations between the ranging performance and fogs are both qualitatively and quantitatively investigated. Furthermore, based on the collected data, a machine learning based model is trained to predict the minimum fog visibility that allows successful ranging for this type of LiDAR. The revealed experimental results and methods are helpful for ToF LiDAR specifications from automotive industry.

For vehicles to navigate autonomously, they need to perceive and understand their immediate surroundings. Currently, cameras are the preferred sensors, due to their high performance and relatively low-cost compared with other sensors like LiDARs and Radars. However, their performance is limited by inherent imaging constraints, a standard RGB camera may perform poorly in extreme conditions, including low illumination, high contrast, bad weather (e.g. fog, rain, snow, etc.), glare, etc. Further, when using monocular cameras, it is more challenging to determine spatial distances than when using active range sensors such as LiDARs or Radars. Over the past years, novel image sensors, namely, infrared cameras, range-gated cameras, polarization cameras, and event cameras, have demonstrated strong potential. Some of them could be game-changers for future autonomous vehicles, they are the result of progress in sensor technology and the development of the accompanying perception algorithms. This paper presents in a systematic manner their principles, comparative advantages, data processing algorithms, and related applications. The purpose is to provide practitioners with an in-depth overview of novel sensing technologies that can contribute to the safe deployment of autonomous vehicles.

Simultaneous localization and mapping problem for mobile robots has received considerable attention over the last decade. The widely used formulation of the SLAM problem has been the augmented state approach in an estimation theoretic framework. Although, many related issues of SLAM such as computational complexity, loop closing and data association have received much attention, the observability issue has largely remained ignored. System observability is an important aspect in any state estimation problem. Observability analysis provides for understanding of the fundamental limits of the solution obtainable, regardless of process and measurement noises. The standard world-centric SLAM formulation is a highly non-linear system. Thus the direct use of linear observability tools and criteria in the analysis of its observability yields incorrect and inconsistent results. In this paper an appropriate method of analysis of the observability of nonlinear systems is applied to investigate the properties of the standard SLAM formulation. Contrary to popular belief, it is shown through theoretical analysis that the standard 2D planar world-centric SLAM formulation involving odometry inputs for robot speed and heading, and range/bearing measurements to features in the environment is unobservable. It is also shown that for the system to be observable, it requires at least two absolutely known feature point positions, thus questioning the very meaning implied by SLAM. The analytical results thus established are verified through simulations