AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints

We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure

Requirement analysis for building practical accident warning systems based on vehicular ad-hoc networks

An Accident Warning System (AWS) is a safety application that provides collision avoidance notifications for next generation vehicles whilst Vehicular Ad-hoc Networks (VANETs) provide the communication functionality to exchange these notifi- cations. Despite much previous research, there is little agreement on the requirements for accident warning systems. In order to build a practical warning system, it is important to ascertain the system requirements, information to be exchanged, and protocols needed for communication between vehicles. This paper presents a practical model of an accident warning system by stipulating the requirements in a realistic manner and thoroughly reviewing previous proposals with a view to identify gaps in this area

Analysis of Driver Behavior Modeling in Connected Vehicle Safety Systems Through High Fidelity Simulation

A critical aspect of connected vehicle safety analysis is understanding the impact of human behavior on the overall performance of the safety system. Given the variation in human driving behavior and the expectancy for high levels of performance, it is crucial for these systems to be flexible to various driving characteristics. However, design, testing, and evaluation of these active safety systems remain a challenging task, exacerbated by the lack of behavioral data and practical test platforms. Additionally, the need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly and time-consuming. As an alternative option, researchers attempt to use simulation platforms to study and evaluate their algorithms. In this work, we introduce a high fidelity simulation platform, designed for a hybrid transportation system involving both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach in simulating a large-scale traffic scenario, making it feasible for extensive studying of automated and active safety systems. Furthermore, we propose a human-interpretable driver model represented as a closed-loop feedback controller. For this model, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of different human-specific and system-specific factors and study their effect on the performance and safety of the traffic network

Automated driving and autonomous functions on road vehicles

In recent years, road vehicle automation has become an important and popular topic for research and development in both academic and industrial spheres. New developments received extensive coverage in the popular press, and it may be said that the topic has captured the public imagination. Indeed, the topic has generated interest across a wide range of academic, industry and governmental communities, well beyond vehicle engineering; these include computer science, transportation, urban planning, legal, social science and psychology. While this follows a similar surge of interest – and subsequent hiatus – of Automated Highway Systems in the 1990’s, the current level of interest is substantially greater, and current expectations are high. It is common to frame the new technologies under the banner of “self-driving cars” – robotic systems potentially taking over the entire role of the human driver, a capability that does not fully exist at present. However, this single vision leads one to ignore the existing range of automated systems that are both feasible and useful. Recent developments are underpinned by substantial and long-term trends in “computerisation” of the automobile, with developments in sensors, actuators and control technologies to spur the new developments in both industry and academia. In this paper we review the evolution of the intelligent vehicle and the supporting technologies with a focus on the progress and key challenges for vehicle system dynamics. A number of relevant themes around driving automation are explored in this article, with special focus on those most relevant to the underlying vehicle system dynamics. One conclusion is that increased precision is needed in sensing and controlling vehicle motions, a trend that can mimic that of the aerospace industry, and similarly benefit from increased use of redundant by-wire actuators

차량간 통신을 이용한 지능형 자동차의 전방차량 위험판단 기법

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 이경수.In recent years, advanced driver assistance systems or highly automated driving systems are expected to enhance road traffic safety, transport efficiency, and driver comfort. Practical applications have become possible due to recent advances in vehicle local sensors and inter vehicle communications. These advances have opened up many possibilities for active safety systems to be more intelligent and robust. The further enhancement of these technologies can be utilized as a risk assessment system of automated drive. This dissertation presents a risk assessment for improved vehicle safety using Front Vehicle Dynamic States through vehicle-to-vehicle wireless communication. A vehicle-to-vehicle wireless communication (V2V communication) has been implemented and fused with a radar sensor to obtain the prediction of remote vehicles motion. Based on the predicted behavior of remote vehicles, a collision risk and a human reaction time are determined for a better driver acceptance and active safety control intervention. A human-centered risk assessment using the V2V communication has been incorporated into a collision avoidance algorithm to monitor threat vehicles ahead and to find the best intervention point. The performance of the proposed algorithm has been investigated via computer simulations and vehicle tests for application to urban and highway driving situation. It has been shown from both simulations and vehicle tests that the proposed integrated risk assessment algorithm with the V2V communication can be beneficial to active safety systems in decision of controller intervention moment and in control of automated drive for the guaranteed safety.Chapter 1 Introduction 1 1.1 Background and Motivations 1 1.2 Previous Researches 5 1.3 Thesis Objectives 9 1.4 Thesis Outline 11 Chapter 2 Vehicular Communication 12 2.1. Literature Review 14 2.1.1 An Empirical Model for V2V communication 14 2.1.2 Position based Sampling and Distance based Interpolation 17 2.2. Communication Delay and Packet Loss Ratio 21 2.2.1 Compensation of V2V Communication Delay 21 Chapter 3 Human Factor Considerations 27 3.1. Driver Acceptance 30 3.1.1 Driver inattention and distraction 31 3.1.2 Mode Confusion 31 3.1.3 Motion Sickness 32 3.2. Sight Distance 33 3.2.1 Stopping Sight Distance 35 3.2.2 Decision Sight Distance 35 Chapter 4 Human-Centered Risk Assessment using Vehicular Wireless Communication 37 4.1. Human-Centered Design 41 4.2. Convergence 43 4.2.1. Sensor-Based Solutions 44 4.2.2. The Benefits to Convergence 45 4.2.3. V2V/Radar Information Fusion 45 4.3. Related Work 46 4.3.1. Radar Sensing Characteristics 47 4.3.2. Probabilistic Threat Assessment 50 4.3.3. Human-Centered Vehicle Control 52 4.3.4. High-Level Information Fusion 54 4.3.5. Target Vehicle State Estimation Performance 58 4.4 Remote Vehicle States Prediction 64 4.5. Collision Risk Analysis 67 4.6. Predicted Collision Distance 70 4.7. Active Safety Intervention Moment Decision 72 Chapter 5 Performance Evaluations 77 5.1. Simulations: MPC based Automated Vehicle Control 78 5.1.1. Effects of V2V Communication on the Controller 78 5.2. Simulations : Human-Centered Risk Assessment 84 5.2.1. Scenarios 84 5.2.2. Effects of V2V Communication: Host vehicle perception only 86 5.2.3. Effects of V2V Communication: Controlled host vehicle 90 5.3. Vehicle Tests 94 5.3.1. Test Vehicle Configuration and Scenario 94 5.3.2. Implementation and Evaluation 96 Chapter 6 Conclusion 99 Bibliography 100 국문초록 110Docto

The Impact of Autonomous Vehicles on Freeway Throughput

Autonomous vehicles are expected to provide a number of benefits to the individual, road infrastructure and the society from the perspective of safety and efficiency. The use of autonomous vehicles is expected to increase freeway throughput, allowing vehicle groups travelling together with a shorter headway time resulting in a reduction of traffic congestion.;The purpose of this research was to use microsimulation software, VISSIM, to test the impact of autonomous vehicles on freeway throughput, delay, and travel time. A realistic corridor of I-79 and a conceptual corridor were modeled to understand how mixed traffic flow conditions could impact the freeway throughput. In addition, the same corridors were used to test the impact of various lane configurations on efficiency of mixed traffic flow including regular and autonomous vehicles.;Our results have shown that incorporation of autonomous vehicles with regular vehicles can increase the freeway throughput. The increase observed in our study has reached above 17% of freeway benefits with 60% or higher of autonomous vehicles penetration rate. However, using autonomous vehicles with lane configuration have shown a negative impact on freeway throughput. That is due to the congestion caused by regular vehicles mainly at the exits and entrances of the freeway

Design and evaluation of safety-critical applications based on inter-vehicle communication

Inter-vehicle communication has a potential to improve road traffic safety and efficiency. Technical feasibility of communication between vehicles has been extensively studied, but due to the scarcity of application-level research, communication\u27s impact on the road traffic is still unclear. This thesis addresses this uncertainty by designing and evaluating two fail-safe applications, namely, Rear-End Collision Avoidance and Virtual Traffic Lights

Multiple vehicle cooperation and collision avoidance in automated vehicles : Survey and an AI‑enabled conceptual framework

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems