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 Light Detection and Ranging (LiDAR) systems in complement to the 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 the main components, from laser transmitter to its beam scanning mechanism. Advantages/disadvantages and the current status of various solutions are introduced and compared. Then, the specific perception pipeline for LiDAR data processing, from an autonomous vehicle perspective is detailed. The model-driven approaches and the emerging deep learning solutions are reviewed. Finally, we provide an overview of the limitations, challenges and trends for automotive LiDARs and perception systems.
Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans
Faculty of Engineering, University of Applied Sciences Aschaffenburg（应用科学阿施芬堡大学工程学院）；Intelligent Embedded Systems Lab, University of Kassel（卡塞尔大学智能嵌入式系统实验室）
Tier IV Inc.（Tier IV公司）；The National Research Institute for Earth Science and Disaster Resilience（国立地球科学与防灾研究所）；Nagoya University（名古屋大学）
Robust sensing and perception in adverse weather conditions remain one of the biggest challenges for realizing reliable autonomous vehicle mobility services. Prior work has established that rainfall rate is a useful measure for the adversity of atmospheric weather conditions. This work presents a probabilistic hierarchical Bayesian model that infers rainfall rate from automotive lidar point cloud sequences with high accuracy and reliability. The model is a hierarchical mixture of experts model, or a probabilistic decision tree, with gating and expert nodes consisting of variational logistic an…
Ibeo Automotive Systems GmbH（Ibeo汽车系统有限公司）；ETH Zurich（苏黎世联邦理工学院）；Stanford University（斯坦福大学）
Neural networks (NNs) are widely used for object classification in autonomous driving. However, NNs can fail on input data not well represented by the training dataset, known as out-of-distribution (OOD) data. A mechanism to detect OOD samples is important for safety-critical applications, such as automotive perception, to trigger a safe fallback mode. NNs often rely on softmax normalization for confidence estimation, which can lead to high confidences being assigned to OOD samples, thus hindering the detection of failures. This paper presents a method for determining whether inputs are OOD, w…
ROS 2-Based LiDAR Perception Framework for Mobile Robots in Dynamic Production Environments, Utilizing Synthetic Data Generation, Transformation-Equivariant 3D Detection and Multi-Object Tracking
Chair of Intelligence in Quality Sensing (IQS), Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University（亚琛工业大学机床与生产工程实验室质量感知智能教席）；The Sirindhorn International Thai-German Graduate School of Engineering (TGGS), King Mongkut’s University of Technology North Bangkok (KMUTNB)（北曼谷先皇技术大学泰德国际工程研究生院）
PERAL: Perception-Aware Motion Control for Passive LiDAR Excitation in Spherical Robots
Centre for Advanced Robotics Technology Innovation (CARTIN), School of Electrical and Electronic Engineering, Nanyang Technological University（先进机器人技术创新中心（CARTIN）、电气与电子工程学院、南洋理工大学）
We present a LiDAR-based and real-time capable 3D perception system for automated driving in urban domains. The hierarchical system design is able to model stationary and movable parts of the environment simultaneously and under real-time conditions. Our approach extends the state of the art by innovative in-detail enhancements for perceiving road users and drivable corridors even in case of non-flat ground surfaces and overhanging or protruding elements. We describe a runtime-efficient pointcloud processing pipeline, consisting of adaptive ground surface estimation, 3D clustering and motion c…
MAN Truck & Bus SE（曼恩卡车与客车公司）；Volkswagen AG（大众汽车公司）
Highly automated driving functions currently often rely on a-priori knowledge from maps for planning and prediction in complex scenarios like cities. This makes map-relative localization an essential skill. In this paper, we address the problem of localization with automotive-grade radars, using a real-time graph-based SLAM approach. The system uses landmarks and odometry information as an abstraction layer. This way, besides radars, all kind of different sensor modalities including cameras and lidars can contribute. A single, semantic landmark map is used and maintained for all sensors. We im…
Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and preprocessing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their …
Modern autonomous vehicles rely heavily on mechanical LiDARs for perception. Current perception methods generally require 360° point clouds, collected sequentially as the LiDAR scans the azimuth and acquires consecutive wedge-shaped slices. The acquisition latency of a full scan (~ 100ms) may lead to outdated perception which is detrimental to safe operation. Recent streaming perception works proposed directly processing LiDAR slices and compensating for the narrow field of view (FOV) of a slice by reusing features from preceding slices. These works, however, are all based on a single modality…
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）
We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic LiDAR data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. Our work creates a precise LiDAR simulator within GTA V which collides with detailed models for all…
University of Edinburgh（爱丁堡大学）；SAIC Motor Technical Center（上汽集团技术中心）
Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy. While estimating the scene flow from LiDAR has progressed recently, it remains largely unknown how to estimate the scene flow from a 4-D radar - an increasingly popular automotive sensor for its robustness against adverse weather and lighting conditions. Compared with the LiDAR point clouds, radar data are drastically sparser, noisier and in much lower resolution. Annotated datasets for radar scene flow are also in absence and costly to acqui…
University of California, Berkeley（加利福尼亚大学伯克利分校）；Ulm University（乌尔姆大学）
Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving. Previous works studied each perception task separately, and lacked a collective quantitative analysis. In this work, we show that it is possible to perform all perception tasks via a simple and efficient multi-task network. Our proposed network, LidarMTL, takes raw LiDAR point cloud as inputs, and predicts six perception outputs for 3D object detection and road understanding. The network is based on an encoder-decoder architecture with 3D sparse co…
DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle
Department of Computer Science and Electronics, Universitas Gadjah Mada（计算机科学与电子系，加查马达大学）；Department of Computer Science and Engineering, Toyohashi University of Technology（计算机科学与工程系，toyohashi技术大学）
Carnegie Mellon University（卡内基梅隆大学）；Amazon（亚马逊）；University of Southern California（南加州大学）
The past few years have witnessed an increasing interest in improving the perception performance of LiDARs on autonomous vehicles. While most of the existing works focus on developing new deep learning algorithms or model architectures, we study the problem from the physical design perspective, i.e., how different placements of multiple LiDARs influence the learning-based perception. To this end, we introduce an easy-to-compute information-theoretic surrogate metric to quantitatively and fast evaluate LiDAR placement for 3D detection of different types of objects. We also present a new data co…
A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems
WMG, University of Warwick（华威大学WMG学院）；School of Electronics, Electrical Engineering and Computer Science at Queen’s University of Belfast（贝尔法斯特女王大学电子、电气工程与计算机科学学院）
LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation
University of Science and Technology Beijing（北京科技大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；The University of Hong Kong（香港大学）；Chengfang Financial Information Technology Services CO.LTD（诚方金融信息技术服务有限公司）
The Hong Kong University of Science and Technology（香港科技大学）；Chinese Academy of Sciences（中国科学院）
High precision 3D LiDARs are still expensive and hard to acquire. This paper presents the characteristics of RS-LiDAR, a model of low-cost LiDAR with sufficient supplies, in comparison with VLP-16. The paper also provides a set of evaluations to analyze the characterizations and performances of LiDARs sensors. This work analyzes multiple properties, such as drift effects, distance effects, color effects and sensor orientation effects, in the context of 3D perception. By comparing with Velodyne LiDAR, we found RS-LiDAR as a cheaper and acquirable substitute of VLP-16 with similar efficiency.
In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals. Enabling robust perception for vehicles requires solving multiple complex problems related to sensor selection/ placement, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. We present PASTA, a novel framework for global co-optimization of deep learning and sensing for dependable vehicle perception. Experimental results with the Audi-TT and BMW-Minicooper vehicles show how PASTA…
LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；National University of Singapore（新加坡国立大学）；Shanghai AI Laboratory（上海人工智能实验室）；Nanjing University of Posts and Telecommunications（南京邮电大学）；S-Lab, Nanyang Technological University（南洋理工大学S-Lab）
Mercedes-Benz AG（梅赛德斯-奔驰集团）；Princeton University（普林斯顿大学）；MPI for Informatics（马克斯·普朗克信息学研究所）
Modeling perception sensors is key for simulation based testing of automated driving functions. Beyond weather conditions themselves, sensors are also subjected to object dependent environmental influences like tire spray caused by vehicles moving on wet pavement. In this work, a novel modeling approach for spray in lidar data is introduced. The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume. The detections are rendered with a simple custom ray casting algorithm without the need of a fluid dynamics simulat…
HumanoidPano: Hybrid Spherical Panoramic-LiDAR Cross-Modal Perception for Humanoid Robots
Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；Beijing Institute of Technology（北京理工大学）；ETH Zurich（苏黎世联邦理工学院）；Nanyang Technological University（南洋理工大学）；X-Humanoid（人形智能科技公司）
Institute of Sensors, Signals and Systems, Heriot-Watt University（赫瑞-瓦特大学传感器、信号与系统研究所）
Datasets for autonomous cars are essential for the development and benchmarking of perception systems. However, most existing datasets are captured with camera and LiDAR sensors in good weather conditions. In this paper, we present the RAdar Dataset In Adverse weaThEr (RADIATE), aiming to facilitate research on object detection, tracking and scene understanding using radar sensing for safe autonomous driving. RADIATE includes 3 hours of annotated radar images with more than 200K labelled road actors in total, on average about 4.6 instances per radar image. It covers 8 different categories of a…
Technische Hochschule Ingolstadt（英戈尔施塔特应用科学大学）；GeneSys Elektronik GmbH（吉内斯电子有限公司）；Universidad de Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）
The trend towards autonomous driving and the continuous research in the automotive area, like Advanced Driver Assistance Systems (ADAS), requires an accurate localization under all circumstances. An accurate estimation of the vehicle state is a basic requirement for any trajectory-planning algorithm. Still, even when the introduction of the GPS L5 band promises lane-accuracy, coverage limitations in roofed areas still have to be addressed. In this work, a method for high precision indoor positioning using a LiDAR is presented. The method is based on the combination of motion models with LiDAR …
State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC)（物联网智能城市国家重点实验室）；Faculty of Science and Technology（科技学院）；University of Macau（澳门大学）；School of Computing and Information Systems（计算与信息学院）；Singapore Management University（新加坡管理大学）；Department of Naval Architecture and Marine Engineering and Department of Robotics（船舶工程与海洋工程系和机器人系）；University of Michigan（密歇根大学）；Department of Robotics（机器人系）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·拉希德智能人工智能大学）
ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception
Centre for Robotics, Mines Paris - PSL, PSL University（巴黎文理研究大学、巴黎矿业学院机器人中心）；U2IS, ENSTA Paris, Institut Polytechnique de Paris（巴黎综合理工学院、巴黎国立高等先进技术学院U2IS实验室）
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（自动化与智能感知学院，上海交通大学）；Global College, Shanghai Jiao Tong University（全球学院，上海交通大学）
LiDAR-based roadside perception is a cornerstone of advanced Intelligent Transportation Systems (ITS). While considerable research has addressed optimal LiDAR placement for infrastructure, the profound impact of differing LiDAR scanning patterns on perceptual performance remains comparatively under-investigated. The inherent nature of various scanning modes - such as traditional repetitive (mechanical/solid-state) versus emerging non-repetitive (e.g. prism-based) systems - leads to distinct point cloud distributions at varying distances, critically dictating the efficacy of object detection an…
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
Shanghai AI Laboratory（上海人工智能实验室）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
