A Systematic Framework to Identify Violations of Scenario-dependent Driving Rules in Autonomous Vehicle Software

2021 ◽  
Vol 5 (2) ◽  
pp. 1-25
Author(s):  
Qingzhao Zhang ◽  
David Ke Hong ◽  
Ze Zhang ◽  
Qi Alfred Chen ◽  
Scott Mahlke ◽  
...  
Keyword(s):  
Real World ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Formal Specifications ◽  
Testing Framework ◽  
Traffic Lights ◽  
Static Code Analysis ◽  
Safety Compliance ◽  
Road Testing ◽  
Systematic Framework

Safety compliance is paramount to the safe deployment of autonomous vehicle (AV) technologies in real-world transportation systems. As AVs will share road infrastructures with human drivers and pedestrians, it is an important requirement for AVs to obey standard driving rules. Existing AV software testing methods, including simulation and road testing, only check fundamental safety rules such as collision avoidance and safety distance. Scenario-dependent driving rules, including crosswalk and intersection rules, are more complicated because the expected driving behavior heavily depends on the surrounding circumstances. However, a testing framework is missing for checking scenario-dependent driving rules on various AV software. In this paper, we design and implement a systematic framework AVChecker for identifying violations of scenario-dependent driving rules in AV software using formal methods. AVChecker represents both the code logic of AV software and driving rules in proposed formal specifications and leverages satisfiability modulo theory (SMT) solvers to identify driving rule violations. To improve the automation of systematic rule-based checking, AVChecker provides a powerful user interface for writing driving rule specifications and applies static code analysis to extract rule-related code logic from the AV software codebase. Evaluations on two open-source AV software platforms, Baidu Apollo and Autoware, uncover 19 true violations out of 28 real-world driving rules covering crosswalks, traffic lights, stop signs, and intersections. Seven of the violations can lead to severe risks of a collision with pedestrians or blocking traffic.

Recent Advances in Reinforcement Learning for Traffic Signal Control

2021 ◽  
Vol 22 (2) ◽  
pp. 12-18 ◽  
Author(s):  
Hua Wei ◽  
Guanjie Zheng ◽  
Vikash Gayah ◽  
Zhenhui Li
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Advantages And Disadvantages ◽  
Recent Advances

Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems

Vision-based Autonomous Vehicle Recognition

2021 ◽  
Vol 54 (4) ◽  
pp. 1-37
Author(s):  
Azzedine Boukerche ◽  
Xiren Ma
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Comprehensive Evaluation ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Regional Security ◽  
Future Research ◽  
Learning Abilities ◽  
Vehicle Recognition ◽  
Model Recognition

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.

Fog-based Intelligent Transportation System for Traffic Light Optimization

2021 ◽  
Vol 58 (S) ◽  
pp. 29-35
Author(s):  
Muhammad Rusyadi Ramli ◽  
Riesa Krisna Astuti Sakir ◽  
Dong-Seong Kim
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Intelligent Transportation System ◽  
Optimization Problems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Light ◽  
Traffic Condition ◽  
Dilemma Zone ◽  
Traffic Lights

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

Big Data Processing and Mining for Next Generation Intelligent Transportation Systems

2013 ◽  
Vol 63 (3) ◽  
Author(s):  
Jelena Fiosina ◽  
Maxims Fiosins, Jörg P. Müller
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Social Impact ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Optimization Techniques ◽  
Next Generation ◽  
Big Data Processing

The deployment of future Internet and communication technologies (ICT) provide intelligent transportation systems (ITS) with huge volumes of real-time data (Big Data) that need to be managed, communicated, interpreted, aggregated and analysed. These technologies considerably enhance the effectiveness and user friendliness of ITS, providing considerable economic and social impact. Real-world application scenarios are needed to derive requirements for software architecture and novel features of ITS in the context of the Internet of Things (IoT) and cloud technologies. In this study, we contend that future service- and cloud-based ITS can largely benefit from sophisticated data processing capabilities. Therefore, new Big Data processing and mining (BDPM) as well as optimization techniques need to be developed and applied to support decision-making capabilities. This study presents real-world scenarios of ITS applications, and demonstrates the need for next-generation Big Data analysis and optimization strategies. Decentralised cooperative BDPM methods are reviewed and their effectiveness is evaluated using real-world data models of the city of Hannover, Germany. We point out and discuss future work directions and opportunities in the area of the development of BDPM methods in ITS.

Mobile Enabled RFID-GPS Based Bus Tracking System

2014 ◽  
Vol 6 (4) ◽  
pp. 10-34 ◽  
Author(s):  
Suresh Sankaranarayanan ◽  
Paul Hamilton
Keyword(s):  
Public Transportation ◽  
Tracking System ◽  
Research Work ◽  
Transportation Systems ◽  
Urban Transport ◽  
Time Of Arrival ◽  
Rfid Technology ◽  
Arrival Times ◽  
Traffic Lights ◽  
Bus Stops

Public transportation in many countries is being used as a means of transport for travelling and accordingly people would prefer these public transportation to be scheduled properly, on time and the frequency be adequately fixed for commuters to make good use of it. It has been found that quite an amount of research work has been carried out, by way of using RFID technology in the public transportation systems towards the tracking of passengers when they board and exit buses. In addition research has also been carried out in using GPS towards the tracking of buses along with RFID technology at traffic lights, bus stops, intersections etc and also displaying expected arrival times on LCD screen at bus stops along with their current positions. Taking these aspects into consideration, an intelligent mobile bus tracking system for the Jamaican Urban Transport Corporation has been proposed and validated as a case study. The proposed system also enables commuters towards tracking the bus of their choice and also knowing their expected arrival times. So taking the above aspects into consideration, in this research the authors have proposed and validated on how control center of a bus company could track the location of a bus based on information received from RFID reader and GPS Transmitter positioned at various Bus stops and in the Bus and accordingly the expected time of arrival calculated for displaying the information on commuter's handset via Gmap. The implementation of the bus tracking scheme has been carried out using Adobe Flash player and Java.

scholarly journals An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Emir Žunić ◽  
Dženana Đonko ◽  
Emir Buza
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Prediction Method ◽  
Transportation Systems ◽  
Data Driven ◽  
Support Vector ◽  
Control Parameters ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Benchmark Instances

Transportation occupies one-third of the amount in the logistics costs, and accordingly transportation systems largely influence the performance of the logistics system. This work presents an adaptive data-driven innovative modular approach for solving the real-world vehicle routing problems (VRPs) in the field of logistics. The work consists of two basic units: (i) an innovative multistep algorithm for successful and entirely feasible solving of the VRPs in logistics and (ii) an adaptive approach for adjusting and setting up parameters and constants of the proposed algorithm. The proposed algorithm combines several data transformation approaches, heuristics, and Tabu search. Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. A comparison of the acquired results has been made using the decision support system with predictive models: generalized linear models (GLMs) and support vector machine (SVM). The algorithm, along with the control parameters, which uses the prediction method, was acquired and was incorporated into a web-based enterprise system, which is in use in several big distribution companies in Bosnia and Herzegovina. The results of the proposed algorithm were compared with a set of benchmark instances and validated over real benchmark instances as well. The successful feasibility of the given routes, in a real environment, is also presented.

scholarly journals Towards a Severity Assessment Method for Potential Cyber Attacks to Connected and Autonomous Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Qiyi He ◽  
Xiaolin Meng ◽  
Rong Qu
Keyword(s):  
Cyber Security ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Assessment Method ◽  
Transportation Systems ◽  
Cyber Attacks ◽  
Large Set ◽  
Security Attacks ◽  
Physical Damage

CAV (connected and autonomous vehicle) is a crucial part of intelligent transportation systems. CAVs utilize both sensors and communication components to make driving decisions. A large number of companies, research organizations, and governments have researched extensively on the development of CAVs. The increasing number of autonomous and connected functions however means that CAVs are exposed to more cyber security vulnerabilities. Unlike computer cyber security attacks, cyber attacks to CAVs could lead to not only information leakage but also physical damage. According to the UK CAV Cyber Security Principles, preventing CAVs from cyber security attacks need to be considered at the beginning of CAV development. In this paper, a large set of potential cyber attacks are collected and investigated from the aspects of target assets, risks, and consequences. Severity of each type of attacks is then analysed based on clearly defined new set of criteria. The levels of severity for the attacks can be categorized as critical, important, moderate, and minor. Mitigation methods including prevention, reduction, transference, acceptance, and contingency are then suggested. It is found that remote control, fake vision on cameras, hidden objects to LiDAR and Radar, spoofing attack to GNSS, and fake identity in cloud authority are the most dangerous and of the highest vulnerabilities in CAV cyber security.

scholarly journals Real-Time Adaptive Traffic Signals Using Rate-Monotonic Scheduling

2019 ◽  
Vol 29 ◽  
pp. 03002 ◽  
Author(s):  
Mãdãlin-Dorin Pop
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Signals ◽  
Fixed Time ◽  
Transportation Systems ◽  
Real Time Scheduling ◽  
Traffic Lights ◽  
Rate Monotonic ◽  
Rate Monotonic Scheduling

The studies and real situations shown that the traffic congestion is one of nowadays highest problems. This problem wassolved in the past using roundabouts and traffic signals. Taking in account the number of cars that is increasing continuously, we can see that past approaches using traffic lights with fixed-time controller for traffic signals timing is obsolete. The present and the future is the using of Intelligent Transportation Systems. Traffic lights systems should be aware about realtime traffic parameters and should adapt accordingly to them. The purpose of this paper is to present a new approach to control traffic signals using rate-monotonic scheduling. Obtained results will be compared with the results obtained by using others real-time scheduling algorithms.

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