Safety is one of the main challenges that prohibit autonomous vehicles (AV), requiring them to be well tested ahead of being allowed on the road. In comparison with road tests, simulators allow us to validate the AV conveniently and affordably. However, it remains unclear how to best use the AV-based simulator system for testing effectively. Our paper presents an empirical testing of AV simulator system that combines the SVL simulator and the Apollo platform. We propose 576 test cases which are inspired by four naturalistic driving situations with pedestrians and surrounding cars. We found that the SVL can imitate realistic safe and collision situations; and at the same time, Apollo can drive the car quite safely. On the other hand, we noted that the system failed to detect pedestrians or vehicles on the road in three out of four classes, accounting for 10.0% total number of scenarios tested. We further applied metamorphic testing to identify inconsistencies in the system with additional 486 test cases. We then discussed some insights into the scenarios that may cause hazardous situations in real life. In summary, this paper provides a new empirical evidence to strengthen the assertion that the simulator-based system can be an indispensable tool for a comprehensive testing of the AV.
Chalmers | University of Gothenburg（查尔姆斯理工大学 | 哥德堡大学）；RWTH Aachen University（亚琛工业大学）
In November 2007 the international competition DARPA Urban Challenge took place on the former George Airforce Base in Victorville, California to significantly promote the research and development on autonomously driving vehicles for urban environments. In the final race only eleven out of initially 89 competitors participated and "Boss" from Carnegie Mellon University succeeded. This paper summarizes results of the research carried out by all finalists within the last five years after the competition and provides an outlook where further investigation especially for software engineering is now…
Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants. This paper presents SUMMIT, a high-fidelity simulator that facilitates the development and testing of crowd-driving algorithms. By leveraging the open-source OpenStreetMap map database and a heterogeneous multi-agent motion prediction model developed in our earlier work, SUMMIT simulates dense, unregulated urban traffic for heterogeneous agents at any worldwide locations that OpenStreetMap supports. SUMMIT is built as an extension of CAR…
This paper demonstrates the integration model-based design approaches or vehicle control, with validation in a freely available open-source simulator. Continued interest in autonomous vehicles and their deployment is driven by the potential benefits of their use. However, it can be challenging to transition new theoretical approaches into unknown simulation environments. Thus, it is critical for experts from other fields, whose insights may be necessary to continue to advance autonomy, to be able to create control applications with the potential to transition to practice. In this article, we w…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Toyota Motor Europe（丰田汽车欧洲公司）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）
Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions. Although recent datasets and trajectory prediction approaches have fostered the development of autonomous vehicles yet the amount of vehicle-pedestrian (bicyclist) interactions modeled are sparse. In this work, we propose Euro-PVI, a dataset of pedestrian and bicyclist traject…
How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off. Understanding human drivers' behavior can be beneficial for autonomous vehicle decision making and planning, especially when the autonomous vehicle is surrounded by human drivers who have various driving behaviors and patterns of interaction with other vehicles. In this paper, we propose an LSTM-based trajectory prediction method for human drivers which can help the autonomous vehicle make better decisions, especially in…
ETH Zürich（苏黎世联邦理工学院）；Sevensense Robotics AG（Sevensense机器人公司）
Visually poor scenarios are one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present MOZARD, a multi-modal localization system for urban outdoor environments using vision and LiDAR. By extending our preexisting key-point based visual multi-session local localization approach with the use of semantic data, an improved localization recall can be achieved across vastly different appearance conditions. In particular we focus on the use of curbstone information because of their broad distribution and reliability within urban en…
Navigating safely in urban environments remains a challenging problem for autonomous vehicles. Occlusion and limited sensor range can pose significant challenges to safely navigate among pedestrians and other vehicles in the environment. Enabling vehicles to quantify the risk posed by unseen regions allows them to anticipate future possibilities, resulting in increased safety and ride comfort. This paper proposes an algorithm that takes advantage of the known road layouts to forecast, quantify, and aggregate risk associated with occlusions and limited sensor range. This allows us to make predi…
The fast urbanization and climate change challenges require solutions that enable the efficient movement of people and goods in cities. We envision future cities to be composed of high-performing walkable districts where transportation needs could be served by fleets of ultra-lightweight shared and autonomous vehicles. A future in which most vehicles would be autonomous creates a new paradigm for the possible interactions between vehicles. Natural swarms are a great example of how rich interactions can be; they can divide tasks, cluster, build together, or transport cooperatively. The field of…
Short-term future of automated driving can be imagined as a hybrid scenario in which both automated and human-driven vehicles co-exist in the same environment. In order to address the needs of such road configuration, many technology solutions such as vehicular communication and predictive control for automated vehicles have been introduced in the literature. Both aforementioned solutions rely on driving data of the human driver. In this work, we investigate the currently available driving datasets and introduce a real-world maneuver-based driving dataset that is collected during our urban dri…
Norwegian University of Science and Technology（挪威科技大学）
In recent years, considerable progress has been made towards a vehicle's ability to operate autonomously. An end-to-end approach attempts to achieve autonomous driving using a single, comprehensive software component. Recent breakthroughs in deep learning have significantly increased end-to-end systems' capabilities, and such systems are now considered a possible alternative to the current state-of-the-art solutions. This paper examines end-to-end learning for autonomous vehicles in simulated urban environments containing other vehicles, traffic lights, and speed limits. Furthermore, the paper…
ETH Zürich（苏黎世联邦理工学院）；Sevensense Robotics AG（Sevensense机器人公司）
Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present VIZARD, a visual localization system for urban outdoor environments. By combining a local localization algorithm with the use of multi-session maps, a high localization recall can be achieved across vastly different appearance conditions. The fusion of the visual localization constraints with wheel-odometry in a state estimation framework further guarantees smooth and accurate pose estimates. In an extensive experimental evaluation on several…
We consider the problem of maximizing distance to road agents for a self-driving car. To this extent, we employ a Model Predictive Control (MPC) approach for the steering tracking control of an Autonomous Vehicle (AV). Specifically, we first present a traditional MPC controller, which is then extended to encode the clearance maximization goal by manipulating its cost function and constraints. We provide insights on the additional information needed to achieve such goal, and how this modifies the structure of the original controller. Furthermore, a connection between commonly used safety metric…
A Vehicle-in-the-Loop Simulator with AI-Powered Digital Twins for Testing Automated Driving Controllers
Department of Electrical Engineering, Eindhoven University of Technology（电子工程系，埃因霍温理工大学）；Siemens Digital Industries Software（西门子数字工业软件）；Department of Mechanical Engineering, Eindhoven University of Technology（机械工程系，埃因霍温理工大学）
University of Michigan Transportation Research Institute（密歇根大学交通研究所）；University of Michigan（密歇根大学）
Current autonomous vehicle (AV) simulators are built to provide large-scale testing required to prove capabilities under varied conditions in controlled, repeatable fashion. However, they have certain failings including the need for user expertise and complex inconvenient tutorials for customized scenario creation. Simulation of Urban Mobility (SUMO) simulator, which has been presented as an open-source AV simulator, is used extensively but suffer from similar issues which make it difficult for entry-level practitioners to utilize the simulator without significant time investment. In that rega…
Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmarks extracted from 3-D lidar data. Our approach features a novel pole detector, a mapping module, and an online localization module, each of which are described in detail, and for which we provide an open-source implementation at www.github.com/acschaefer/polex. In extensive experiments, we demonstrate that our method improves on the state of the ar…
Netherlands Organisation for Applied Scientific Research（荷兰应用科学研究组织）；Eindhoven University of Technology（埃因霍温理工大学）
The urban environment is amongst the most difficult domains for autonomous vehicles. The vehicle must be able to plan a safe route on challenging road layouts, in the presence of various dynamic traffic participants such as vehicles, cyclists and pedestrians and in various environmental conditions. The challenge remains to have motion planners that are computationally fast and that account for future movements of other road users proactively. This paper describes an computationally efficient sampling-based trajectory planner for safe and comfortable driving in urban environments. The planner i…
Evaluation and Optimization of Adaptive Cruise Control in Autonomous Vehicles using the CARLA Simulator: A Study on Performance under Wet and Dry Weather Conditions
Al-Ahliyya Amman University（安曼阿赫利亚大学）；University of Jordan（约旦大学）
In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations. This is achieved by building a differentiable data-driven simulator on top of perception outputs and high-fidelity HD maps of the area. It allows us to synthesize new driving experiences from existing demonstrations using mid-level representations. Using this simulator we then train a policy network in closed-loop employing policy gradients. We train our proposed method on 100 hours of expert demonstrations on urb…
UrbanV2X: A Multisensory Vehicle-Infrastructure Dataset for Cooperative Navigation in Urban Areas
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University（航空与航空工程系，香港理工大学）；Hong Kong Applied Science and Technology Research Institute (ASTRI)（香港应用科学和技术研究院）；Nanyang Technological University（南洋理工大学）
Safe mobility for unmanned ground vehicles requires reliable detection of other vehicles, along with precise estimates of their locations and trajectories. Here we describe the algorithms and system we have developed for accurate trajectory estimation of nearby vehicles using an onboard scanning LADAR. We introduce a variable-axis Ackerman steering model and compare this to an independent steering model. Then for robust tracking we propose a multi-hypothesis tracker that combines these kinematic models to leverage the strengths of each. When trajectories estimated with our techniques are input…
The energy of ocean waves is the key distinguishing factor of marine environments compared to other aquatic environments such as lakes and rivers. Waves significantly affect the dynamics of marine vehicles; hence it is imperative to consider the dynamics of vehicles in waves when developing efficient control strategies for autonomous surface vehicles (ASVs). However, most marine simulators available open-source either exclude dynamics of vehicles in waves or use methods with high computational overhead. This paper presents ASVLite, a computationally efficient ASV simulator that uses frequency …
Computer Vision Center（计算机视觉中心）；Intel Labs（英特尔研究院）；Toyota Research Institute（丰田研究院）
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an…
Massachusetts Institute of Technology（麻省理工学院）；CTU in Prague（布拉格捷克理工大学）
Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is revie…
The 2007 DARPA Urban Challenge afforded the golden opportunity for the Technische Universität Braunschweig to demonstrate its abilities to develop an autonomously driving vehicle to compete with the world's best competitors. After several stages of qualification, our team CarOLO qualified early for the DARPA Urban Challenge Final Event and was among only eleven teams from initially 89 competitors to compete in the final. We had the ability to work together in a large group of experts, each contributing his expertise in his discipline, and significant organisational, financial and technical sup…
Rendering accurate multisensory feedback is critical to ensure natural user behavior in driving simulators. In this work, we present a virtual reality (VR)-based Vehicle-in-the-Loop (ViL) simulator that provides visual, vestibular, and haptic feedback to drivers in high speed driving conditions. Designing our simulator around a four-wheel steer-by-wire vehicle enables us to emulate the dynamics of a vehicle traveling significantly faster than the test vehicle and to transmit corresponding haptic steering feedback to the driver. By scaling the speed of the test vehicle through a combination of …
In the past few years, the European Commission has financed several projects to examine how new technologies could improve the sustainability of European cities. These technologies concern new public transportation modes such as guided buses to form high capacity networks similar to light rail but at a lower cost and better flexibility, PRT (Personal Rapid Transit) and cybercars (small urban vehicles with fully automatic driving capabilities to be used in carsharing mode, mostly as a complement to mass transport). They also concern private vehicles with technologies which could improve the eff…
Imitation learning is becoming more and more successful for autonomous driving. End-to-end (raw signal to command) performs well on relatively simple tasks (lane keeping and navigation). Mid-to-mid (environment abstraction to mid-level trajectory representation) or direct perception (raw signal to performance) approaches strive to handle more complex, real life environment and tasks (e.g. complex intersection). In this work, we show that complex urban situations can be handled with raw signal input and mid-level representation. We build a hybrid end-to-mid approach predicting trajectories for …
This paper presents the most thorough study to date of vehicular carrier-phase differential GNSS (CDGNSS) positioning performance in a deep urban setting unaided by complementary sensors. Using data captured during approximately 2 hours of driving in and around the dense urban center of Austin, TX, a CDGNSS system is demonstrated to achieve 17-cm-accurate 3D urban positioning (95% probability) with solution availability greater than 87%. The results are achieved without any aiding by inertial, electro-optical, or odometry sensors. Development and evaluation of the unaided GNSS-based precise po…
This paper presents the development of a real-time simulator for the validation of controlling a large vehicle manipulator. The need for this development can be justified by the lack of such a simulator: There are neither open source projects nor commercial products, which would be suitable for testing cooperative control concepts. First, we present the nonlinear simulation model of the vehicle and the manipulator. For the modeling MATLAB/Simulink is used, which also enables a code generation into standalone C++ ROS-Nodes (Robot Operating System Nodes). The emerging challenges of the code gene…
New York University（纽约大学）；University of Pennsylvania（宾夕法尼亚大学）；CTU in Prague（捷克布拉格技术大学）
Autonomous mobile robots have the potential to solve missions that are either too complex or dangerous to be accomplished by humans. In this paper, we address the design and autonomous deployment of a ground vehicle equipped with a robotic arm for urban firefighting scenarios. We describe the hardware design and algorithm approaches for autonomous navigation, planning, fire source identification and abatement in unstructured urban scenarios. The approach employs on-board sensors for autonomous navigation and thermal camera information for source identification. A custom electro{mechanical pump…
As autonomous driving technology is getting more and more mature today, autonomous delivery companies like Starship, Marble, and Nuro has been making progress in the tests of their autonomous delivery robots. While simulations and simulators are very important for the final product landing of the autonomous delivery robots since the autonomous delivery robots need to navigate on the sidewalk, campus, and other urban scenarios, where the simulations can avoid real damage to pedestrians and properties in the real world caused by any algorithm failures and programming errors and thus accelerate t…
Robust and lane-level positioning is essential for autonomous vehicles. As an irreplaceable sensor, LiDAR can provide continuous and high-frequency pose estimation by means of mapping, on condition that enough environment features are available. The error of mapping can accumulate over time. Therefore, LiDAR is usually integrated with other sensors. In diverse urban scenarios, the environment feature availability relies heavily on the traffic (moving and static objects) and the degree of urbanization. Common LiDAR-based SLAM demonstrations tend to be studied in light traffic and less urbanized…
Stanford University（斯坦福大学）；Honda Research Institute（本田研究所）
Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rapid changes in driver behavior. Providing an efficient strategy to navigate through urban intersections is a difficult task. This paper frames the problem of navigating unsignalized intersections as a partially observable Markov decision process (POMDP) and solves it using a Monte Carlo sampling method. Empirical results in simulation show that the resulting policy outperforms a threshold-based heuristic strategy on s…
Institute of Robotics and Machine Intelligence（机器人与机器智能研究所）；Poznan University of Technology（波兹南理工大学）
An efficient path planner for autonomous car-like vehicles should handle the strong kinematic constraints, particularly in confined spaces commonly encountered while maneuvering in city traffic, and should enable rapid planning, as the city traffic scenarios are highly dynamic. State-of-the-art planning algorithms handle such difficult cases at high computational cost, often yielding non-deterministic results. However, feasible local paths can be quickly generated leveraging the past planning experience gained in the same or similar environment. While learning through supervised training is pr…
