It is well known that semantic segmentation can be used as an effective intermediate representation for learning driving policies. However, the task of street scene semantic segmentation requires expensive annotations. Furthermore, segmentation algorithms are often trained irrespective of the actual driving task, using auxiliary image-space loss functions which are not guaranteed to maximize driving metrics such as safety or distance traveled per intervention. In this work, we seek to quantify the impact of reducing segmentation annotation costs on learned behavior cloning agents. We analyze several segmentation-based intermediate representations. We use these visual abstractions to systematically study the trade-off between annotation efficiency and driving performance, i.e., the types of classes labeled, the number of image samples used to learn the visual abstraction model, and their granularity (e.g., object masks vs. 2D bounding boxes). Our analysis uncovers several practical insights into how segmentation-based visual abstractions can be exploited in a more label efficient manner. Surprisingly, we find that state-of-the-art driving performance can be achieved with orders of magnitude reduction in annotation cost. Beyond label efficiency, we find several additional training benefits when leveraging visual abstractions, such as a significant reduction in the variance of the learned policy when compared to state-of-the-art end-to-end driving models.
End-to-end approaches to autonomous driving have high sample complexity and are difficult to scale to realistic urban driving. Simulation can help end-to-end driving systems by providing a cheap, safe, and diverse training environment. Yet training driving policies in simulation brings up the problem of transferring such policies to the real world. We present an approach to transferring driving policies from simulation to reality via modularity and abstraction. Our approach is inspired by classic driving systems and aims to combine the benefits of modular architectures and end-to-end deep lear…
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；East China Normal University（华东师范大学）；Chinese University of Hong Kong（香港中文大学）；Zhejiang University（浙江大学）
Recent developments in autonomous driving technology have proven that map data may be used, not only for general routing purposes, but also for to enhance and complement common sensor data. This document reviews the most commonly used interfaces and formats at each step of a selfhealing map data chain.
Beijing Institute of Technology（北京理工大学）；Delft University of Technology（代尔夫特理工大学）
Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years. This paper gives a brief summary of state-of-the-art autonomous driving strategies at intersections. Firstly, we enumerate and analyze common types of intersection scenarios, corresponding simulation platforms, as well as related datasets. Secondly, by reviewing previous studies, we have summarized characteristics of existing autonomous driving strategies and class…
Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques
University of North Texas（北德克萨斯大学）；Fordham University（福特汉姆大学）；Qatar University（卡塔尔大学）；University of Nebraska at Kearney（内布拉斯加大学卡尼尔分校）；Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学（MBZUAI））；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
Our autonomous driving simulation lab produces a high-precision 3D model simulating the parking lot. However, the current model still has poor rendering quality in some aspects. In this work, we develop a system to improve the rendering of the model and evaluate the quality of the rendered model.
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
Zhongguancun Laboratory（中关村实验室）；Beihang University（北京航空航天大学）；Institute of Data Space, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）；Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；A*STAR（新加坡科技研究局）
Owing to the merits of early safety and reliability guarantee, autonomous driving virtual testing has recently gains increasing attention compared with closed-loop testing in real scenarios. Although the availability and quality of autonomous driving datasets and toolsets are the premise to diagnose the autonomous driving system bottlenecks and improve the system performance, due to the diversity and privacy of the datasets and toolsets, collecting and featuring the perspective and quality of them become not only time-consuming but also increasingly challenging. This paper first proposes a Sys…
ENSTA Paris（巴黎高等先进技术学院）；Institut Polytechnique de Paris（巴黎理工学院）；INRIA（法国国家信息与自动化研究所）；Université Paris-Saclay（巴黎萨克雷大学）；CEA（法国原子能和替代能源委员会）；List（李斯特研究所）
In recent years, we have witnessed increasingly high performance in the field of autonomous end-to-end driving. In particular, more and more research is being done on driving in urban environments, where the car has to follow high level commands to navigate. However, few evaluations are made on the ability of these agents to react in an unexpected situation. Specifically, no evaluations are conducted on the robustness of driving agents in the event of a bad high-level command. We propose here an evaluation method, namely a benchmark that allows to assess the robustness of an agent, and to appr…
This survey reviews explainability methods for vision-based self-driving systems trained with behavior cloning. The concept of explainability has several facets and the need for explainability is strong in driving, a safety-critical application. Gathering contributions from several research fields, namely computer vision, deep learning, autonomous driving, explainable AI (X-AI), this survey tackles several points. First, it discusses definitions, context, and motivation for gaining more interpretability and explainability from self-driving systems, as well as the challenges that are specific t…
Reinforcement learning has become one of the most trending subjects in the recent decade. It has seen applications in various fields such as robot manipulations, autonomous driving, path planning, computer gaming, etc. We accomplished three tasks during the course of this project. Firstly, we studied the Q-learning algorithm for tabular environments and applied it successfully to an OpenAi Gym environment, Taxi. Secondly, we gained an understanding of and implemented the deep Q-network algorithm for Cart-Pole environment. Thirdly, we also studied the application of reinforcement learning in au…
Autonomous driving has been among the most popular and challenging topics in the past few years. On the road to achieving full autonomy, researchers have utilized various sensors, such as LiDAR, camera, Inertial Measurement Unit (IMU), and GPS, and developed intelligent algorithms for autonomous driving applications such as object detection, object segmentation, obstacle avoidance, and path planning. High-definition (HD) maps have drawn lots of attention in recent years. Because of the high precision and informative level of HD maps in localization, it has immediately become one of the critica…
A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving
Dr. Ing. h.c. F. Porsche AG（保时捷股份公司）；Forschungszentrum Informatik（弗劳恩霍夫信息技术研究中心）；Hochschule Esslingen（埃斯林根应用技术大学）；University of Michigan（密歇根大学）；Voxel51；Karlsruher Institut für Technologie（卡尔斯鲁厄理工学院）
With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered and unseen environments with high uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors, that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize Driveability in some quantif…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
In recent years, control under urban intersection scenarios becomes an emerging research topic. In such scenarios, the autonomous vehicle confronts complicated situations since it must deal with the interaction with social vehicles timely while obeying the traffic rules. Generally, the autonomous vehicle is supposed to avoid collisions while pursuing better efficiency. The existing work fails to provide a framework that emphasizes the integrity of the scenarios while being able to deploy and test reinforcement learning(RL) methods. Specifically, we propose a benchmark for training and testing …
The objective of the first CARLA autonomous driving challenge was to deploy autonomous driving systems to lead with complex traffic scenarios where all participants faced the same challenging traffic situations. According to the organizers, this competition emerges as a way to democratize and to accelerate the research and development of autonomous vehicles around the world using the CARLA simulator contributing to the development of the autonomous vehicle area. Therefore, this paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment t…
This paper introduces the autonomous system of the "Smart Shark II" which won the Formula Student Autonomous China (FSAC) Competition in 2018. In this competition, an autonomous racecar is required to complete autonomously two laps of unknown track. In this paper, the author presents the self-driving software structure of this racecar which ensure high vehicle speed and safety. The key components ensure a stable driving of the racecar, LiDAR-based and Vision-based cone detection provide a redundant perception; the EKF-based localization offers high accuracy and high frequency state estimation;…
The next-generation high-resolution automotive radar (4D radar) can provide additional elevation measurement and denser point clouds, which has great potential for 3D sensing in autonomous driving. In this paper, we introduce a dataset named TJ4DRadSet with 4D radar points for autonomous driving research. The dataset was collected in various driving scenarios, with a total of 7757 synchronized frames in 44 consecutive sequences, which are well annotated with 3D bounding boxes and track ids. We provide a 4D radar-based 3D object detection baseline for our dataset to demonstrate the effectivenes…
With the development of autonomous driving, the improvement of autonomous driving technology for individual vehicles has reached the bottleneck. The advancement of vehicle-road cooperation autonomous driving technology can expand the vehicle's perception range, supplement the perception blind area and improve the perception accuracy, to promote the development of autonomous driving technology and achieve vehicle-road integration. This project mainly uses lidar to develop data fusion schemes to realize the sharing and combination of vehicle and road equipment data and achieve the detection and …
University of Surrey（萨里大学）；University of Warwick（华威大学）；Warwick Manufacturing Group（华威制造集团）
Autonomous driving decision-making is a challenging task due to the inherent complexity and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake at any time to pass a slow vehicle or to help traffic flow. Anticipating the intention of surrounding vehicles, estimating their future states and integrating them into the decision-making process of an automated vehicle can enhance the reliability of autonomous driving in complex driving scenarios. This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model that considers the man…
Autonomous driving is a multi-task problem requiring a deep understanding of the visual environment. End-to-end autonomous systems have attracted increasing interest as a method of learning to drive without exhaustively programming behaviours for different driving scenarios. When humans drive, they rely on a finely tuned sensory system which enables them to quickly acquire the information they need while filtering unnecessary details. This ability to identify task-specific high-interest regions within an image could be beneficial to autonomous driving agents and machine learning systems in gen…
To improve the security and robustness of autonomous driving models, this paper presents SMET, a scenariobased metamorphic testing tool for autonomous driving models. The metamorphic relationship is divided into three dimensions (time, space, and event) and demonstrates its effectiveness through case studies in two types of autonomous driving models with different outputs.Experimental results show that this tool can well detect potential defects of the autonomous driving model, and complex scenes are more effective than simple scenes.
Autonomous Driving Small-Scale Cars: A Survey of Recent Development
Chair of Econometrics and Statistics, esp. in the Transport Sector, Technische Universität Dresden（经济计量学与统计学教授席位（侧重交通领域））；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig（可扩展数据分析与人工智能中心（ScaDS.AI）德累斯顿/莱比锡）；Barkhausen Institut gGmbH（巴克豪森研究所）
LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving
Department of Civil and Environmental Engineering, University of Michigan（土木与环境工程系，密歇根大学）；University of Michigan Transportation Research Institute（密歇根大学交通研究所）
State-of-the-art autonomous driving systems rely on high definition (HD) maps for localization and navigation. However, building and maintaining HD maps is time-consuming and expensive. Furthermore, the HD maps assume structured environment such as the existence of major road and lanes, which are not present in rural areas. In this work, we propose an end-to-end transformer networks based approach for map-less autonomous driving. The proposed model takes raw LiDAR data and noisy topometric map as input and produces precise local trajectory for navigation. We demonstrate the effectiveness of ou…
South China University of Technology（华南理工大学）；PerceptIn Inc（PerceptIn公司）；University of Shanghai for Science and Technology（上海理工大学）；Université de Cergy-Pontoise（塞尔吉-蓬图瓦兹大学）；Clarkson University（克拉克森大学）；Wayne State University（韦恩州立大学）；University of California, Irvine（加利福尼亚大学欧文分校）
Autonomous driving is not one single technology but rather a complex system integrating many technologies, which means that teaching autonomous driving is a challenging task. Indeed, most existing autonomous driving classes focus on one of the technologies involved. This not only fails to provide a comprehensive coverage, but also sets a high entry barrier for students with different technology backgrounds. In this paper, we present a modular, integrated approach to teaching autonomous driving. Specifically, we organize the technologies used in autonomous driving into modules. This is describe…
Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane tracking. In this study, an autonomous driving system is developed and tested in the experimental environment designed for this purpose. In this system, a model vehicle having a camera is used to trace the lanes and avoid obstacles to experimentally study autonomous driving behavior. Convolutional Neural Network models were trained for Lane tracking. For the vehic…
PerceptIn（感知创新公司）；South China University of Technology（华南理工大学）；Northwestern University（西北大学）
The infrastructure-vehicle cooperative autonomous driving approach depends on the cooperation between intelligent roads and intelligent vehicles. This approach is not only safer but also more economical compared to the traditional on-vehicle-only autonomous driving approach. In this paper, we introduce our real-world deployment experiences of cooperative autonomous driving, and delve into the details of new challenges and opportunities. Specifically, based on our progress towards commercial deployment, we follow a three-stage development roadmap of the cooperative autonomous driving approach:i…
We describe the computing tasks involved in autonomous driving, examine existing autonomous driving computing platform implementations. To enable autonomous driving, the computing stack needs to simultaneously provide high performance, low power consumption, and low thermal dissipation, at low cost. We discuss possible approaches to design computing platforms that will meet these needs.
Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；Department of Systems Design Engineering, University of Waterloo（滑铁卢大学系统设计工程系）；David Cheriton school of Computer science, University of Waterloo（滑铁卢大学戴维·谢里顿计算机学院）；University of Waterloo（滑铁卢大学）
This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
University of Southern California（南加州大学）；Virginia Tech（弗吉尼亚理工大学）；Miami University（迈阿密大学）；Northern Arizona University（北亚利桑那大学）；Northeastern University（东北大学）
World Models for Autonomous Driving: An Initial Survey
University of Macau（澳门大学）；College of Transportation Engineering, Tongji University（同济大学交通运输工程学院）；Tsinghua University（清华大学）；Macau University of Science and Technology（澳门科技大学）；University of Hawaii at Manoa（夏威夷大学马诺阿分校）
