Offloading time-sensitive, computationally intensive tasks-such as advanced learning algorithms for autonomous driving-from vehicles to nearby edge servers, vehicle-to-infrastructure (V2I) systems, or other collaborating vehicles via vehicle-to-vehicle (V2V) communication enhances service efficiency. However, whence traversing the path to the destination, the vehicle's mobility necessitates frequent handovers among the access points (APs) to maintain continuous and uninterrupted wireless connections to maintain the network's Quality of Service (QoS). These frequent handovers subsequently lead to task migrations among the edge servers associated with the respective APs. This paper addresses the joint problem of task migration and access-point handover by proposing a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm. A joint allocation method of communication and computation of APs is proposed to minimize computational load, service latency, and interruptions with the overarching goal of maximizing QoS. We implement and evaluate our proposed framework on simulated experiments to achieve smooth and seamless task switching among edge servers, ultimately reducing latency.
Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy. This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles. Firstly, the basic outline of decision-making technology is provided. Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision…
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 …
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…
Risk is traditionally described as the expected likelihood of an undesirable outcome, such as collisions for autonomous vehicles. Accurately predicting risk or potentially risky situations is critical for the safe operation of autonomous vehicles. In our previous work, we showed that risk could be characterized by two components: 1) the probability of an undesirable outcome and 2) an estimate of how undesirable the outcome is (loss). This paper is an extension to our previous work. In this paper, using our trained deep reinforcement learning model for navigating around crowds, we developed a r…
Dense urban traffic environments can produce situations where accurate prediction and dynamic models are insufficient for successful autonomous vehicle motion planning. We investigate how an autonomous agent can safely negotiate with other traffic participants, enabling the agent to handle potential deadlocks. Specifically we consider merges where the gap between cars is smaller than the size of the ego vehicle. We propose a game theoretic framework capable of generating and responding to interactive behaviors. Our main contribution is to show how game-tree decision making can be executed by a…
University of Goettingen（哥廷根大学）；Nankai University（南开大学）
With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to efficiently train autonomous vehicles. Through a security abidi…
We were able to complete the full integration of the Robotic Technology Kernel (RTK) into an electric vehicle by-wire system using lidar and GPS sensors. The solution included a mobile application to interface with the RTK-enabled autonomous vehicle. Altogether the system was designed to be modular, using the concepts of message-based software design that is built into the Robot Operating System (ROS), which is at the foundation of RTK. The team worked incrementally to develop working software to demonstrate each milestone on the path to successfully completing the RTK integration for the deve…
In this tutorial, we detailed simple controllers for autonomous parking and path following for self-driving cars providing practical methods for curvature computation.
Trinity College Dublin（都柏林圣三一学院）；College Green（学院绿地）
Policy gradient is an effective way to estimate continuous action on the environment. This paper, it about explaining the mathematical formula and code implementation. In the end, comparing between the rotation angle of the stick on CartPole , and the angle of the Autonomous vehicle when turning, and utilizing the Bicycle Model, a simple Kinematic dynamic model, are the purpose to discover the similarity between these two models, so as to facilitate the model transfer from CartPole to the F1tenth Autonomous vehicle.
With the recent development of autonomous vehicle technology, there have been active efforts on the deployment of this technology at different scales that include urban and highway driving. While many of the prototypes showcased have been shown to operate under specific cases, little effort has been made to better understand their shortcomings and generalizability to new areas. Distance, uptime and number of manual disengagements performed during autonomous driving provide a high-level idea on the performance of an autonomous system but without proper data normalization, testing location infor…
We present a control approach for autonomous vehicles based on deep reinforcement learning. A neural network agent is trained to map its estimated state to acceleration and steering commands given the objective of reaching a specific target state while considering detected obstacles. Learning is performed using state-of-the-art proximal policy optimization in combination with a simulated environment. Training from scratch takes five to nine hours. The resulting agent is evaluated within simulation and subsequently applied to control a full-size research vehicle. For this, the autonomous explor…
The coordination of multiple autonomous vehicles into convoys or platoons is expected on our highways in the near future. However, before such platoons can be deployed, the new autonomous behaviors of the vehicles in these platoons must be certified. An appropriate representation for vehicle platooning is as a multi-agent system in which each agent captures the "autonomous decisions" carried out by each vehicle. In order to ensure that these autonomous decision-making agents in vehicle platoons never violate safety requirements, we use formal verification. However, as the formal verification t…
This paper proposes a sampling based planning algorithm to control autonomous vehicles. We propose an improved Rapidly-exploring Random Tree which includes the definition of K- nearest points and propose a two-stage sampling strategy to adjust RRT in other to perform maneuver while avoiding collision. The simulation results show the success of the algorithm.
Johannes Kepler University Linz（林茨约翰内斯·开普勒大学）；UAS Technikum Wien（维也纳技术应用科学大学）
In this paper, we present our brand-new platform for Automated Driving research. The chosen vehicle is a RAV4 hybrid SUV from TOYOTA provided with exteroceptive sensors such as a multilayer LIDAR, a monocular camera, Radar and GPS; and proprioceptive sensors such as encoders and a 9-DOF IMU. These sensors are integrated in the vehicle via a main computer running ROS1 under Linux 20.04. Additionally, we installed an open-source ADAS called Comma Two, that runs Openpilot to control the vehicle. The platform is currently being used to research in the field of autonomous vehicles, human and autono…
Understanding how people view and interact with autonomous vehicles is important to guide future directions of research. One such way of aiding understanding is through simulations of virtual environments involving people and autonomous vehicles. We present a simulation model that incorporates people and autonomous vehicles in a shared urban space. The model is able to simulate many thousands of people and vehicles in real-time. This is achieved by use of GPU hardware, and through a novel linear program solver optimized for large numbers of problems on the GPU. The model is up to 30 times fast…
University of Michigan（密歇根大学）；University of Michigan Transportation Research Institute（密歇根大学交通研究所）；Mcity
In this paper, we reveal that the rarity of safety-critical events in high-dimensional driving environments is the root cause of the safety challenge for autonomous vehicle development. We formulate it as "curse of rarity" (CoR) because it occurs ubiquitously in various safety-critical systems such as medical diagnosis and aerospace systems.
Testing autonomous vehicles in simulation environments is crucial. Sim-ATAV is an open-source framework developed for experimenting with different test generation techniques in simulation environments for research purposes. This document provides a tutorial on Sim-ATAV with a running example.
FACHHOCHSCHULE LAUSITZ University of Applied Sciences（劳西茨应用技术大学）
This diploma thesis describes the theoretical bases, the conception of the module and the final result of the development process in application. for the environment logging with a small mobile robot for interiors should be sketched an economical alternative to the expensive laser scanners. the structure, color or the material of the objects in the radius of action, as well as the environment brightness and illuminating are to have thereby no influence on the results of measurement.
Faculdade de Engenharia da Universidade do Porto（波尔图大学工程学院）；CRACS/INESC-TEC；Faculdade de Ciências da Universidade do Porto（波尔图大学理学院）
We present Dolphin, an extensible programming language for autonomous vehicle networks. A Dolphin program expresses an orchestrated execution of tasks defined compositionally for multiple vehicles. Building upon the base case of elementary one-vehicle tasks, the built-in operators include support for composing tasks in several forms, for instance according to concurrent, sequential, or event-based task flow. The language is implemented as a Groovy DSL, facilitating extension and integration with external software packages, in particular robotic toolkits. The paper describes the Dolphin languag…
University of Toronto（多伦多大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）
We present two behavioral models of an autonomous vehicle and its interaction with the environment. Both models use the formal modeling language LNT provided by the CADP toolbox. This paper discusses the modeling choices and the challenges of our autonomous vehicle models, and also illustrates how formal validation tools can be applied to a single component or the overall vehicle.
PerceptIn Inc.（PerceptIn公司）；South China University of Technology（华南理工大学）
PerceptIn develops and commercializes autonomous vehicles for micromobility around the globe. This paper makes a holistic summary of PerceptIn's development and operating experiences. This paper provides the business tale behind our product, and presents the development of the computing system for our vehicles. We illustrate the design decision made for the computing system, and show the advantage of offloading localization workloads onto an FPGA platform.
University of Michigan（密歇根大学）；Bilkent University（比尔肯特大学）
In this paper, we propose a decision making algorithm for autonomous vehicle control at a roundabout intersection. The algorithm is based on a game-theoretic model representing the interactions between the ego vehicle and an opponent vehicle, and adapts to an online estimated driver type of the opponent vehicle. Simulation results are reported.
University of Pennsylvania（宾夕法尼亚大学）；ETH Zurich（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）；Technical University of Munich（慕尼黑工业大学）；University of Virginia（弗吉尼亚大学）；Clemson University（克莱姆森大学）
The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware for high performance race vehicles which aim to operate autonomously on the edge of the vehicles limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods and approac…
As autonomous vehicles (AVs) inch closer to reality, a central requirement for acceptance will be earning the trust of humans in everyday driving situations. In particular, the interaction between AVs and pedestrians is of high importance, as every human is a pedestrian at some point of the day. This paper considers the interaction of a pedestrian and an autonomous vehicle at a mid-block, unsignalized intersection where there is ambiguity over when the pedestrian should cross and when and how the vehicle should yield. By modeling pedestrian behavior through the concept of gap acceptance, the a…
We present a scalable distributed target tracking algorithm based on the alternating direction method of multipliers that is well-suited for a fleet of autonomous cars communicating over a vehicle-to-vehicle network. Each sensing vehicle communicates with its neighbors to execute iterations of a Kalman filter-like update such that each agent's estimate approximates the centralized maximum a posteriori estimate without requiring the communication of measurements. We show that our method outperforms the Consensus Kalman Filter in recovering the centralized estimate given a fixed communication ba…
Safety-critical Autonomous Systems require trustworthy and transparent decision-making process to be deployable in the real world. The advancement of Machine Learning introduces high performance but largely through black-box algorithms. We focus the discussion of explainability specifically with Autonomous Vehicles (AVs). As a safety-critical system, AVs provide the unique opportunity to utilize cutting-edge Machine Learning techniques while requiring transparency in decision making. Interpretability in every action the AV takes becomes crucial in post-hoc analysis where blame assignment might…
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Delaware（特拉华大学）；University of Chinese Academy of Sciences（中国科学院大学）
Infrastructure-Vehicle Cooperative Autonomous Driving (IVCAD) is a new paradigm of autonomous driving, which relies on the cooperation between intelligent roads and autonomous vehicles. This paradigm has been shown to be safer and more efficient compared to the on-vehicle-only autonomous driving paradigm. Our real-world deployment data indicates that the effectiveness of IVCAD is constrained by reliability and performance of commercial communication networks. This paper targets this exact problem, and proposes INTERNEURON, a middleware to achieve high communication reliability between intellig…
Safe autonomous vehicles must be able to predict and react to the drivers around them. Previous control methods rely heavily on pre-computation and are unable to react to dynamic events as they unfold in real-time. In this paper, we extend Model Predictive Path Integral Control (MPPI) using differential game theory and introduce Best-Response MPPI (BR-MPPI) for real-time multi-vehicle interactions. Experimental results are presented using two AutoRally platforms in a racing format with BR-MPPI competing against a skilled human driver at the Georgia Tech Autonomous Racing Facility.
This paper describes a novel method for allowing an autonomous ground vehicle to predict the intent of other agents in an urban environment. This method, termed the cognitive driving framework, models both the intent and the potentially false beliefs of an obstacle vehicle. By modeling the relationships between these variables as a dynamic Bayesian network, filtering can be performed to calculate the intent of the obstacle vehicle as well as its belief about the environment. This joint knowledge can be exploited to plan safer and more efficient trajectories when navigating in an urban environm…
Autonomous Vehicles(AV) are one of the brightest promises of the future which would help cut down fatalities and improve travel time while working in harmony. Autonomous vehicles will face with challenging situations and experiences not seen before. These experiences should be converted to knowledge and help the vehicle prepare better in the future. Online Transfer Learning will help transferring prior knowledge to a new task and also keep the knowledge updated as the task evolves. This paper presents the different methods of transfer learning, online transfer learning and organic computing th…
With the development of autonomous driving, it is becoming increasingly common for autonomous vehicles (AVs) and human-driven vehicles (HVs) to travel on the same roads. Existing single-vehicle planning algorithms on board struggle to handle sophisticated social interactions in the real world. Decisions made by these methods are difficult to understand for humans, raising the risk of crashes and making them unlikely to be applied in practice. Moreover, vehicle flows produced by open-source traffic simulators suffer from being overly conservative and lacking behavioral diversity. We propose a h…
Tsinghua University（清华大学）；Anhui Jianghuai Automobile Co Ltd（安徽江淮汽车集团股份有限公司）
Autonomous vehicles must be comprehensively evaluated before deployed in cities and highways. However, most existing evaluation approaches for autonomous vehicles are static and lack adaptability, so they are usually inefficient in generating challenging scenarios for tested vehicles. In this paper, we propose an adaptive evaluation framework to efficiently evaluate autonomous vehicles in adversarial environments generated by deep reinforcement learning. Considering the multimodal nature of dangerous scenarios, we use ensemble models to represent different local optimums for diversity. We then…
This paper describes Ariel Team's autonomous racing controller for the Indy Autonomous Challenge (IAC) simulation race. IAC is the first multi-vehicle autonomous head-to-head competition, reaching speeds of 300 km/h along an oval track, modeled after the Indianapolis Motor Speedway (IMS). Our racing controller attempts to maximize progress along the track while avoiding collisions with opponent vehicles and obeying the race rules. To this end, the racing controller first computes a race line offline. Then, it repeatedly computes online a small set of dynamically feasible maneuver candidates, e…
Institute for Digital Technologies, Loughborough University（拉夫堡大学数字技术研究所）
Due to the complexity of the natural world, a programmer cannot foresee all possible situations, a connected and autonomous vehicle (CAV) will face during its operation, and hence, CAVs will need to learn to make decisions autonomously. Due to the sensing of its surroundings and information exchanged with other vehicles and road infrastructure, a CAV will have access to large amounts of useful data. While different control algorithms have been proposed for CAVs, the benefits brought about by connectedness of autonomous vehicles to other vehicles and to the infrastructure, and its implications …
Since early 2020, the coronavirus disease 2019 (COVID-19) has spread rapidly across the world. As at the date of writing this article, the disease has been globally reported in 223 countries and regions, infected over 108 million people and caused over 2.4 million deaths (https://covid19.who.int/, accessed on Feb. 17, 2021). Avoiding person-to-person transmission is an effective approach to control and prevent the pandemic. However, many daily activities, such as transporting goods in our daily life, inevitably involve person-to-person contact. Using an autonomous logistic vehicle to achieve c…
Amrita School of Engineering（阿姆里塔工程学院）；Amrita Vishwa Vidyapeetham（阿姆里塔维迪亚佩瑟姆大学）
Advanced driver assistance systems are advancing at a rapid pace and all major companies started investing in developing the autonomous vehicles. But the security and reliability is still uncertain and debatable. Imagine that a vehicle is compromised by the attackers and then what they can do. An attacker can control brake, accelerate and even steering which can lead to catastrophic consequences. This paper gives a very short and brief overview of most of the possible attacks on autonomous vehicle software and hardware and their potential implications.
Manoeuvring in the presence of emergency vehicles is still a major issue for vehicle autonomy systems. Most studies that address this topic are based on rule-based methods, which cannot cover all possible scenarios that can take place in autonomous traffic. Multi-Agent Proximal Policy Optimisation (MAPPO) has recently emerged as a powerful method for autonomous systems because it allows for training in thousands of different situations. In this study, we present an approach based on MAPPO to guarantee the safe and efficient manoeuvring of autonomous vehicles in the presence of an emergency veh…
University of Melbourne（墨尔本大学）；Metropolitan Fire Brigade（大都会消防局）
An autonomous indoor aerial vehicle requires reliable simul- taneous localization and mapping (SLAM), accurate flight control, and robust path planning for navigation. This paper presents a system level combination of these existing technologies for 2D navigation. An Unmanned aerial vehicle (UAV) called URSA (Unmanned Recon and Safety Aircraft) that can autonomously flight and mapping indoors environments with an accuracy of 2 cm was developed. Performance in indoor environments was assessed in terms of mapping and navigation precision.
An open problem for autonomous driving is how to validate the safety of an autonomous vehicle in simulation. Automated testing procedures can find failures of an autonomous system but these failures may be difficult to interpret due to their high dimensionality and may be so unlikely as to not be important. This work describes an approach for finding interpretable failures of an autonomous system. The failures are described by signal temporal logic expressions that can be understood by a human, and are optimized to produce failures that have high likelihood. Our methodology is demonstrated for…
Unmanned vehicle technologies are an area of great interest in theory and practice today. These technologies have advanced considerably after the first applications have been implemented and cause a rapid change in human life. Autonomous vehicles are also a big part of these technologies. The most important action of a driver has to do is to follow the lanes on the way to the destination. By using image processing and artificial intelligence techniques, an autonomous vehicle can move successfully without a driver help. They can go from the initial point to the specified target by applying pre-…
University of Michigan（密歇根大学）；Ford Motor Company（福特汽车公司）
Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into. In this paper, we consider the problem of autonomous vehicle control for forced merge scenarios. We propose a novel game-theoretic controller, called the Leader-Follower Game Controller (LFGC), in which the interactions between the autonomous ego vehicle and other vehicles with a priori uncertain driving intentions is modeled as a partially observable…
College of Automotive Engineering, Chongqing University（重庆大学汽车工程学院）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 mobility学院）；University of Waterloo（滑铁卢大学）
This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL). First, the highway driving environment is built, wherein the ego vehicle, surrounding vehicles, and road lanes are included. Then, the overtaking decision-making problem of the automated vehicle is formulated as an optimal control problem. Then relevant control actions, state variables, and optimization objectives are elaborated. Finally, the deep Q-network is applied to derive the intelligent driving policies for the ego vehicle. Simulation results reveal that the ego ve…
Korea Advanced Institute of Science and Technologies (KAIST)（韩国科学技术院）
Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as o…
