Applied Sciences

Research

Jump to: Review, Other

15 pages, 58173 KiB

Open AccessArticle

A Study on the Evaluation Method of Highway Driving Assist System Using Monocular Camera

by Geon Hwan Bae and Seon Bong Lee

Appl. Sci. 2020, 10(18), 6443; https://doi.org/10.3390/app10186443 - 16 Sep 2020

Cited by 4 | Viewed by 2390

Abstract

In this paper, we propose a method to evaluate Highway Driving Assist (HDA) systems, a type of Advanced Driver Assistance System (ADAS), using a monocular camera, which eliminates the need of experts or expensive equipment and reduces the time, effort, and cost required [...] Read more.

In this paper, we propose a method to evaluate Highway Driving Assist (HDA) systems, a type of Advanced Driver Assistance System (ADAS), using a monocular camera, which eliminates the need of experts or expensive equipment and reduces the time, effort, and cost required in such tests. We use the information from the images captured by the monocular camera, such as the lane and rear tires of the lead vehicle, and from the geometrical composition, including the heading angle and the distance of the camera from the front bumper of the test vehicle. To verify the evaluation method, we used the image and geometric information to calculate the distances of the lead vehicle and the lane center from the test vehicle. We compared and analyzed the method using DGPS (Differential Global Positioning System), Data Acquisition(DAQ) and the method using monocular camera. Therefore, it was determined that the proposed method of evaluating HDA systems using a monocular camera is reliable because of the small margin of error between the theoretical with monocular camera and real vehicle test with DAQ and DGPS. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

19 pages, 1674 KiB

Open AccessArticle

Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics

by Wilfried Wöber, Georg Novotny, Lars Mehnen and Cristina Olaverri-Monreal

Appl. Sci. 2020, 10(18), 6317; https://doi.org/10.3390/app10186317 - 10 Sep 2020

Cited by 7 | Viewed by 3277

Abstract

On-board sensory systems in autonomous vehicles make it possible to acquire information about the vehicle itself and about its relevant surroundings. With this information the vehicle actuators are able to follow the corresponding control commands and behave accordingly. Localization is thus a critical [...] Read more.

On-board sensory systems in autonomous vehicles make it possible to acquire information about the vehicle itself and about its relevant surroundings. With this information the vehicle actuators are able to follow the corresponding control commands and behave accordingly. Localization is thus a critical feature in autonomous driving to define trajectories to follow and enable maneuvers. Localization approaches using sensor data are mainly based on Bayes filters. Whitebox models that are used to this end use kinematics and vehicle parameters, such as wheel radii, to interfere the vehicle’s movement. As a consequence, faulty vehicle parameters lead to poor localization results. On the other hand, blackbox models use motion data to model vehicle behavior without relying on vehicle parameters. Due to their high non-linearity, blackbox approaches outperform whitebox models but faulty behaviour such as overfitting is hardly identifiable without intensive experiments. In this paper, we extend blackbox models using kinematics, by inferring vehicle parameters and then transforming blackbox models into whitebox models. The probabilistic perspective of vehicle movement is extended using random variables representing vehicle parameters. We validated our approach, acquiring and analyzing simulated noisy movement data from mobile robots and vehicles. Results show that it is possible to estimate vehicle parameters with few kinematic assumptions. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

12 pages, 1181 KiB

Open AccessFeature PaperArticle

Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry

by Francisco Miguel Moreno, Carlos Guindel, José María Armingol and Fernando García

Appl. Sci. 2020, 10(16), 5657; https://doi.org/10.3390/app10165657 - 14 Aug 2020

Cited by 1 | Viewed by 2042

Abstract

This paper presents a study of how the performance of LiDAR odometry is affected by the preprocessing of the point cloud through the use of 3D semantic segmentation. The study analyzed the estimated trajectories when the semantic information is exploited to filter the [...] Read more.

This paper presents a study of how the performance of LiDAR odometry is affected by the preprocessing of the point cloud through the use of 3D semantic segmentation. The study analyzed the estimated trajectories when the semantic information is exploited to filter the original raw data. Different filtering configurations were tested: raw (original point cloud), dynamic (dynamic obstacles are removed from the point cloud), dynamic vehicles (vehicles are removed), far (distant points are removed), ground (the points belonging to the ground are removed) and structure (only structures and objects are kept in the point cloud). The experiments were performed using the KITTI and SemanticKITTI datasets, which feature different scenarios that allowed identifying the implications and relevance of each element of the environment in LiDAR odometry algorithms. The conclusions obtained from this work are of special relevance for improving the efficiency of LiDAR odometry algorithms in all kinds of scenarios. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

20 pages, 11428 KiB

Open AccessArticle

A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector

by Miguel Ángel de Miguel, Francisco Miguel Moreno, Pablo Marín-Plaza, Abdulla Al-Kaff, Martín Palos, David Martín, Rodrigo Encinar-Martín and Fernando García

Appl. Sci. 2020, 10(16), 5655; https://doi.org/10.3390/app10165655 - 14 Aug 2020

Cited by 8 | Viewed by 3628

Abstract

This work presents a novel platform for autonomous vehicle technologies research for the insurance sector. The platform has been collaboratively developed by the insurance company MAPFRE-CESVIMAP, Universidad Carlos III de Madrid and INSIA of the Universidad Politécnica de Madrid. The high-level architecture and [...] Read more.

This work presents a novel platform for autonomous vehicle technologies research for the insurance sector. The platform has been collaboratively developed by the insurance company MAPFRE-CESVIMAP, Universidad Carlos III de Madrid and INSIA of the Universidad Politécnica de Madrid. The high-level architecture and several autonomous vehicle technologies developed using the framework of this collaboration are introduced and described in this work. Computer vision technologies for environment perception, V2X communication capabilities, enhanced localization, human–machine interaction and self awareness are among the technologies which have been developed and tested. Some use cases that validate the technologies presented in the platform are also presented; these use cases include public demonstrations, tests of the technologies and international competitions for self-driving technologies. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

12 pages, 1625 KiB

Open AccessArticle

Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers

by Hanwool Woo, Mizuki Sugimoto, Hirokazu Madokoro, Kazuhito Sato, Yusuke Tamura, Atsushi Yamashita and Hajime Asama

Appl. Sci. 2020, 10(9), 3289; https://doi.org/10.3390/app10093289 - 8 May 2020

Cited by 3 | Viewed by 3083

Abstract

In this paper, we propose a novel method to estimate a goal of surround vehicles to perform a lane change at a merging section. Recently, autonomous driving and advance driver-assistance systems are attracting great attention as a solution to substitute human drivers and [...] Read more.

In this paper, we propose a novel method to estimate a goal of surround vehicles to perform a lane change at a merging section. Recently, autonomous driving and advance driver-assistance systems are attracting great attention as a solution to substitute human drivers and to decrease accident rates. For example, a warning system to alert a lane change performed by surrounding vehicles to the front space of the host vehicle can be considered. If it is possible to forecast the intention of the interrupting vehicle in advance, the host driver can easily respond to the lane change with sufficient reaction time. This paper assumes a mandatory situation where two lanes are merged. The proposed method assesses the interaction between the lane-changing vehicle and the host vehicle on the mainstream lane. Then, the lane-change goal is estimated based on the interaction under the assumption that the lane-changing driver decides to minimize the collision risk. The proposed method applies the dynamic potential field method, which changes the distribution according to the relative speed and distance between two subject vehicles, to assess the interaction. The performance of goal estimation is evaluated using real traffic data, and it is demonstrated that the estimation can be successfully performed by the proposed method. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

15 pages, 1497 KiB

Open AccessArticle

Simulation of the Daily Activity Plans of Travelers Using the Park-and-Ride System and Autonomous Vehicles: Work and Shopping Trip Purposes

by Jairo Ortega, Jamil Hamadneh, Domokos Esztergár-Kiss and János Tóth

Appl. Sci. 2020, 10(8), 2912; https://doi.org/10.3390/app10082912 - 23 Apr 2020

Cited by 39 | Viewed by 3990

Abstract

The preferences of travelers determines the utility of daily activity plans. Decision-makers can affect the preference of travelers when they force private car users to use park-and-ride (P&R) facilities as a way of decreasing traffic in city centers. The P&R system has been [...] Read more.

The preferences of travelers determines the utility of daily activity plans. Decision-makers can affect the preference of travelers when they force private car users to use park-and-ride (P&R) facilities as a way of decreasing traffic in city centers. The P&R system has been shown to be effective in reducing uninterrupted increases in traffic congestion, especially in city centers. Therefore, the impacts of P&R on travel behavior and the daily activity plans of both worker and shopper travelers were studied in this paper. Moreover, autonomous vehicles (AVs) are a promising technology for the coming decade. A simulation of the AV as part of a multimodal system, when the P&R system was integrated in the daily activity plans, was carried out to determine the required AV fleet size needed to fulfill a certain demand and to study the impacts of AVs on the behavior of travelers (trip time and distance). Specifically, a group of travelers, who use private cars as their transport mode, was studied, and certain modifications to their daily activity plans, including P&R facilities and changing their transport mode, were introduced. Using the MATSim open-source tool, four scenarios were simulated based on the mentioned modifications. The four scenarios included (1) a simulation of the existing transport modes of the travelers, (2) a simulation of their daily activity plans when their transport modes were changed to AVs, (3) a simulation of the travelers, when P&R facilities were included in their activity chain plans, and (4) a simulation of their daily activity plans, when both P&R and AVs were included in their activity chain plans. The result showed that using the P&R system increased overall travel time, compared with using a private car. The results also demonstrated that using AVs as a replacement for conventional cars reduced travel time. In conclusion, the impact of P&R and AVs on the travel behavior of certain travelers was evaluated in this paper. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

13 pages, 2537 KiB

Open AccessArticle

An Investigation of Early Detection of Driver Drowsiness Using Ensemble Machine Learning Based on Hybrid Sensing

by Jongseong Gwak, Akinari Hirao and Motoki Shino

Appl. Sci. 2020, 10(8), 2890; https://doi.org/10.3390/app10082890 - 22 Apr 2020

Cited by 46 | Viewed by 5285

Abstract

Drowsy driving is one of the main causes of traffic accidents. To reduce such accidents, early detection of drowsy driving is needed. In previous studies, it was shown that driver drowsiness affected driving performance, behavioral indices, and physiological indices. The purpose of this [...] Read more.

Drowsy driving is one of the main causes of traffic accidents. To reduce such accidents, early detection of drowsy driving is needed. In previous studies, it was shown that driver drowsiness affected driving performance, behavioral indices, and physiological indices. The purpose of this study is to investigate the feasibility of classification of the alert states of drivers, particularly the slightly drowsy state, based on hybrid sensing of vehicle-based, behavioral, and physiological indicators with consideration for the implementation of these identifications into a detection system. First, we measured the drowsiness level, driving performance, physiological signals (from electroencephalogram and electrocardiogram results), and behavioral indices of a driver using a driving simulator and driver monitoring system. Next, driver alert and drowsy states were identified by machine learning algorithms, and a dataset was constructed from the extracted indices over a period of 10 s. Finally, ensemble algorithms were used for classification. The results showed that the ensemble algorithm can obtain 82.4% classification accuracy using hybrid methods to identify the alert and slightly drowsy states, and 95.4% accuracy classifying the alert and moderately drowsy states. Additionally, the results show that the random forest algorithm can obtain 78.7% accuracy when classifying the alert vs. slightly drowsy states if physiological indicators are excluded and can obtain 89.8% accuracy when classifying the alert vs. moderately drowsy states. These results represent the feasibility of highly accurate early detection of driver drowsiness and the feasibility of implementing a driver drowsiness detection system based on hybrid sensing using non-contact sensors. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

14 pages, 4009 KiB

Open AccessArticle

Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network

by Seungyo Ryu, Dongseung Kim and Joongheon Kim

Appl. Sci. 2020, 10(6), 1938; https://doi.org/10.3390/app10061938 - 12 Mar 2020

Cited by 7 | Viewed by 2526

Abstract

This study proposes a novel multi-module deep neural network framework which aims at improving intelligent long-term traffic forecasting. Following our previous system, the internal architecture of the new system adds deep learning modules that enable data separation during computation. Thus, prediction becomes more [...] Read more.

This study proposes a novel multi-module deep neural network framework which aims at improving intelligent long-term traffic forecasting. Following our previous system, the internal architecture of the new system adds deep learning modules that enable data separation during computation. Thus, prediction becomes more accurate in many sections of the road network and gives dependable results even under possible changes in weather conditions during driving. The performance of the framework is then evaluated for different cases, which include all plausible cases of driving, i.e., regular days, holidays, and days involving severe weather conditions. Compared with other traffic predicting systems that employ the convolutional neural networks, k-nearest neighbor algorithm, and the time series model, it is concluded that the system proposed herein achieves better performance and helps drivers schedule their trips well in advance. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

16 pages, 4475 KiB

Open AccessArticle

Loss of Control Prediction for Motorcycles during Emergency Braking Maneuvers Using a Supervised Learning Algorithm

by Pedro Huertas-Leyva, Giovanni Savino, Niccolò Baldanzini and Marco Pierini

Appl. Sci. 2020, 10(5), 1754; https://doi.org/10.3390/app10051754 - 4 Mar 2020

Cited by 8 | Viewed by 3261

Abstract

The most common evasive maneuver among motorcycle riders and one of the most complicated to perform in emergency situations is braking. Because of the inherent instability of motorcycles, motorcycle crashes are frequently caused by loss of control performing braking as an evasive maneuver. [...] Read more.

The most common evasive maneuver among motorcycle riders and one of the most complicated to perform in emergency situations is braking. Because of the inherent instability of motorcycles, motorcycle crashes are frequently caused by loss of control performing braking as an evasive maneuver. Understanding the motion conditions that lead riders to start losing control is essential for defining countermeasures capable of minimizing the risk of this type of crashes. This paper provides predictive models to classify unsafe loss of control braking maneuvers on a straight line before becoming irreversibly unstable. We performed braking maneuver experiments in the field with motorcycle riders facing a simulated emergency scenario. The latter involved a mock-up intersection in which we generated conflict events between the motorcycle ridden by the participants and an oncoming car driven by trained research staff. The data collected comprises 165 braking trials (including 11 trials identified as loss of control) with 13 riders representing four categories of braking skill, ranging from beginner to expert. Three predictive models of loss of control events during braking trials, going from a basic model to a more advanced one, were defined using logistic regressions as supervised learning methods and using the area under the receiver operating characteristic (ROC) curve as a performance indicator. The predictor variables of the models were identified among the parameters of the vehicle kinematics. The best model predicted 100% of the loss of control and 100% of the full control cases. The basic and the more advanced supervised models were adapted for loss of control identification with time series data, and the results detecting in real-time the loss of control events showed excellent performance as well as with the supervised models. The study showed that expert riders may maintain stability under dynamic conditions that normally lead less skilled riders to a loss of control or falling events. The best decision thresholds of the most relevant kinematic parameters to predict loss of control have been defined. The thresholds of parameters that typically characterize the loss of control such as the yaw rate and front-wheel lock duration were dependent on the rider skill levels. The peak-to-root-mean-square ratio of roll acceleration was the most robust parameter for identifying loss of control among all skill levels. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

17 pages, 1910 KiB

Open AccessArticle

Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change

by Xiaodong Wu, Bangjun Qiao and Chengrui Su

Appl. Sci. 2020, 10(5), 1626; https://doi.org/10.3390/app10051626 - 29 Feb 2020

Cited by 9 | Viewed by 3412

Abstract

A lane change is one of the most important driving scenarios for autonomous driving vehicles. This paper proposes a safe and comfort-oriented algorithm for an autonomous vehicle to perform lane changes on a straight and level road. A simplified Gray Prediction Model is [...] Read more.

A lane change is one of the most important driving scenarios for autonomous driving vehicles. This paper proposes a safe and comfort-oriented algorithm for an autonomous vehicle to perform lane changes on a straight and level road. A simplified Gray Prediction Model is designed to estimate the driving status of surrounding vehicles, and time-variant safety margins are employed during the trajectory planning to ensure a safe maneuver. The algorithm is able to adapt its lane changing strategy based on traffic situation and passenger demands, and features condition-triggered rerouting to handle unexpected traffic situations. The concept of dynamic safety margins with different settings of parameters gives a customizable feature for the autonomous lane changing control. The effect of the algorithm is verified within a self-developed traffic simulation system. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

22 pages, 4206 KiB

Open AccessArticle

A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

by Zhonggui Zhang, Yi Ming and Gangbing Song

Appl. Sci. 2020, 10(5), 1625; https://doi.org/10.3390/app10051625 - 29 Feb 2020

Cited by 4 | Viewed by 3858

Abstract

In this paper we develop a new approach to directly detect crash hotspot intersections (CHIs) using two customized spatial weights matrices, which are the inverse network distance-band spatial weights matrix of intersections (INDSWMI) and the k-nearest distance-band spatial weights matrix between crash and [...] Read more.

In this paper we develop a new approach to directly detect crash hotspot intersections (CHIs) using two customized spatial weights matrices, which are the inverse network distance-band spatial weights matrix of intersections (INDSWMI) and the k-nearest distance-band spatial weights matrix between crash and intersection (KDSWMCI). This new approach has three major steps. The first step is to build the INDSWMI by forming the road network, extracting the intersections from road junctions, and constructing the INDSWMI with road network constraints. The second step is to build the KDSWMCI by obtaining the adjacency crashes for each intersection. The third step is to perform intersection hotspot analysis (IHA) by using the Getis–Ord Gi* statistic with the INDSWMI and KDSWMCI to identify CHIs and test the Intersection Prediction Accuracy Index (IPAI). This approach is validated by comparison of the IPAI obtained using open street map (OSM) roads and intersection-related crashes (2008–2017) from Spencer city, Iowa, USA. The findings of the comparison show that higher prediction accuracy is achieved by using the proposed approach in identifying CHIs. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

17 pages, 12901 KiB

Open AccessArticle

Double Deep Q-Network with a Dual-Agent for Traffic Signal Control

by Jianfeng Gu, Yong Fang, Zhichao Sheng and Peng Wen

Appl. Sci. 2020, 10(5), 1622; https://doi.org/10.3390/app10051622 - 29 Feb 2020

Cited by 32 | Viewed by 3976

Abstract

Adaptive traffic signal control (ATSC) based on deep reinforcement learning (DRL) has shown promising prospects to reduce traffic congestion. Most existing methods keeping traffic signal phases fixed adopt two agent actions to match a four-phase suffering unstable performance and undesirable operation in a [...] Read more.

Adaptive traffic signal control (ATSC) based on deep reinforcement learning (DRL) has shown promising prospects to reduce traffic congestion. Most existing methods keeping traffic signal phases fixed adopt two agent actions to match a four-phase suffering unstable performance and undesirable operation in a four-phase signalized intersection. In this paper, a Double Deep Q-Network (DDQN) with a dual-agent algorithm is proposed to obtain a stable traffic signal control policy. Specifically, two agents are denoted by two different states and shift the control of green lights to make the phase sequence fixed and control process stable. State representations and reward functions are presented by improving the observability and reducing the leaning difficulty of two agents. To enhance the feasibility and reliability of two agents in the traffic control of the four-phase signalized intersection, a network structure incorporating DDQN is proposed to map states to rewards. Experiments under Simulation of Urban Mobility (SUMO) are carried out, and results show that the proposed traffic signal control algorithm is effective in improving traffic capacity. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

19 pages, 5650 KiB

Open AccessArticle

Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach

by Zhonggui Zhang, Yi Ming and Gangbing Song

Appl. Sci. 2019, 9(24), 5282; https://doi.org/10.3390/app9245282 - 4 Dec 2019

Cited by 7 | Viewed by 4235

Abstract

This paper develops a three-step spatial data mining approach to directly identify road clusters with high-frequency crashes (RCHC). The first step, preprocessing, is to store the roads and crashes in a spatial database. The second step is to describe the conceptualization of road–road [...] Read more.

This paper develops a three-step spatial data mining approach to directly identify road clusters with high-frequency crashes (RCHC). The first step, preprocessing, is to store the roads and crashes in a spatial database. The second step is to describe the conceptualization of road–road and crash–road spatial relationships. The spatial weight matrix of roads (SWMR) is constructed to describe the conceptualization of road–road spatial relationships. The conceptualization of crash–road spatial relationships is established using crash spatial aggregation algorithm. The third step, spatial data mining, is to identify RCHC using the cluster and outlier analysis (local Moran’s I index). This approach was validated using spatial data set including roads and road-related crashes (2008–2018) from Polk County, IOWA, U.S.A. The findings of this research show that the proposed approach is successful in identifying RCHC and road outliers. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

19 pages, 130009 KiB

Open AccessArticle

Measuring Method of Node Importance of Urban Rail Network Based on H Index

by Xiaolu Li, Peng Zhang and Guangyu Zhu

Appl. Sci. 2019, 9(23), 5189; https://doi.org/10.3390/app9235189 - 29 Nov 2019

Cited by 8 | Viewed by 2553

Abstract

Urban rail stations play an important role in passenger distribution and connectivity intervals in the network. How to effectively and reasonably evaluate their importance in the network is the key to optimizing the urban rail network structure and reducing operational risks. Taking the [...] Read more.

Urban rail stations play an important role in passenger distribution and connectivity intervals in the network. How to effectively and reasonably evaluate their importance in the network is the key to optimizing the urban rail network structure and reducing operational risks. Taking the site as the research object and considering the topology, passenger volume, and passenger flow correlation of the urban rail network, the index of citation index is used to define node importance metric based on the h-index. Furthermore, the method of calculating the importance degree of urban rail transit network nodes based on h-index is proposed. The validity of the method is verified by the data of Beijing urban rail network in 2016, and the results are compared with the existing central index based on the network topology characteristics. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

23 pages, 4259 KiB

Open AccessArticle

Autonomous Driving—A Crash Explained in Detail

by Johannes Betz, Alexander Heilmeier, Alexander Wischnewski, Tim Stahl and Markus Lienkamp

Appl. Sci. 2019, 9(23), 5126; https://doi.org/10.3390/app9235126 - 26 Nov 2019

Cited by 15 | Viewed by 8667

Abstract

Since 2017, a research team from the Technical University of Munich has developed a software stack for autonomous driving. The software was used to participate in the Roborace Season Alpha Championship. The championship aims to achieve autonomous race cars competing with different software [...] Read more.

Since 2017, a research team from the Technical University of Munich has developed a software stack for autonomous driving. The software was used to participate in the Roborace Season Alpha Championship. The championship aims to achieve autonomous race cars competing with different software stacks against each other. In May 2019, during a software test in Modena, Italy, the greatest danger in autonomous driving became reality: A minor change in environmental influences led an extensively tested software to crash into a barrier at speed. Crashes with autonomous vehicles have happened before but a detailed explanation of why software failed and what part of the software was not working correctly is missing in research articles. In this paper we present a general method that can be used to display an autonomous vehicle disengagement to explain in detail what happened. This method is then used to display and explain the crash from Modena. Firstly a brief introduction into the modular software stack that was used in the Modena event, consisting of three individual parts—perception, planning, and control—is given. Furthermore, the circumstances causing the crash are elaborated in detail.By presented and explaining in detail which software part failed and contributed to the crash we can discuss further software improvements. As a result, we present necessary functions that need to be integrated in an autonomous driving software stack to prevent such a vehicle behavior causing a fatal crash. In addition we suggest an enhancement of the current disengagement reports for autonomous driving regarding a detailed explanation of the software part that was causing the disengagement. In the outlook of this paper we present two additional software functions for assessing the tire and control performance of the vehicle to enhance the autonomous. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

14 pages, 3343 KiB

Open AccessArticle

Anti-Congestion Route Planning Scheme Based on Dijkstra Algorithm for Automatic Valet Parking System

by Luyang Yu, Haobin Jiang and Lei Hua

Appl. Sci. 2019, 9(23), 5016; https://doi.org/10.3390/app9235016 - 21 Nov 2019

Cited by 22 | Viewed by 3729

Abstract

Based on the Dijkstra algorithm, with the parking parameters in the static state, the shortest route to each parking space of the parking lot without dynamic influence factors can be calculated. In the new technology background of the combination of the V2X environment [...] Read more.

Based on the Dijkstra algorithm, with the parking parameters in the static state, the shortest route to each parking space of the parking lot without dynamic influence factors can be calculated. In the new technology background of the combination of the V2X environment and driverless technology, the dynamic influence factors, for example, the lanes occupancy situation caused by parking, can be considered to improve the shortest route with the new scheme in this paper. Then the final route that costs the least time to reach each parking space will be calculated. This is very important for the development of the intelligent transportation system in the parking lot environment. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

18 pages, 2271 KiB

Open AccessArticle

Advanced Adaptive Cruise Control Based on Operation Characteristic Estimation and Trajectory Prediction

by Hanwool Woo, Hirokazu Madokoro, Kazuhito Sato, Yusuke Tamura, Atsushi Yamashita and Hajime Asama

Appl. Sci. 2019, 9(22), 4875; https://doi.org/10.3390/app9224875 - 14 Nov 2019

Cited by 12 | Viewed by 3574

Abstract

In this paper, we propose an advanced adaptive cruise control to evaluate the collision risk between adjacent vehicles and adjust the distance between them seeking to improve driving safety. As a solution for preventing crashes, an autopilot vehicle has been considered. In the [...] Read more.

In this paper, we propose an advanced adaptive cruise control to evaluate the collision risk between adjacent vehicles and adjust the distance between them seeking to improve driving safety. As a solution for preventing crashes, an autopilot vehicle has been considered. In the near future, the technique to forecast dangerous situations and automatically adjust the speed to prevent a collision can be implemented to a real vehicle. We have attempted to realize the technique to predict the future positions of adjacent vehicles. Several previous studies have investigated similar approaches; however, these studies ignored the individual characteristics of drivers and changes in driving conditions, even though the prediction performance largely depends on these characteristics. The proposed method allows estimating the operation characteristics of each driver and applying the estimated results to obtain the trajectory prediction. Then, the collision risk is evaluated based on such prediction. A novel advanced adaptive cruise control, proposed in this paper, adjusts its speed and distance from adjacent vehicles accordingly to minimize the collision risk in advance. In evaluation using real traffic data, the proposed method detected lane changes with 99.2% and achieved trajectory prediction error of 0.065 m, on average. In addition, it was demonstrated that almost 35% of the collision risk can be decreased by applying the proposed method compared to that of human drivers. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

26 pages, 5023 KiB

Open AccessArticle

Simulation of Metro Congestion Propagation Based on Route Choice Behaviors Under Emergency-Caused Delays

by Xingchuan Wang, Enjian Yao and Shasha Liu

Appl. Sci. 2019, 9(20), 4210; https://doi.org/10.3390/app9204210 - 9 Oct 2019

Cited by 5 | Viewed by 2478

Abstract

Generally, metro emergencies could lead to delays and seriously affect passengers’ trips. The dynamic congestion propagation process under metro emergency-caused delays could be regarded as the aggregation of passengers’ individual travel choices. This paper aims to simulate the congestion propagation process without intervention [...] Read more.

Generally, metro emergencies could lead to delays and seriously affect passengers’ trips. The dynamic congestion propagation process under metro emergency-caused delays could be regarded as the aggregation of passengers’ individual travel choices. This paper aims to simulate the congestion propagation process without intervention measures under the metro emergency-caused delays, which is integrated with passengers’ route choice behaviors. First, using a stated preference survey data collected from Guangzhou Metro (GZM) passengers, route choice models are developed based on random regret minimization (RRM) theory under metro emergency conditions. Then, a simulation environment is established using graph cellular automata (graph-CA) with augmented GZM network structure, where an ASEIR (advanced susceptible-exposed-infectious-recovered) model with time delay is proposed as the evolution rule in graph-CA. Furthermore, considering passengers’ routing preferences, a quantified method for the congestion propagation rate is proposed, and the congestion propagation process on a subnetwork of the GZM network is simulated. The simulation results show that metro congestion during peak periods has a secondary increase after the end of the emergency-caused delays, while the congestion during nonpeak hours has a shorter duration and a smaller influence range. The proposed simulation model could clearly reflect the dynamic process of congestion propagation under metro emergencies. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

16 pages, 4299 KiB

Open AccessArticle

Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA

by Xiaolu Li, Xi Zhang, Peng Zhang and Guangyu Zhu

Appl. Sci. 2019, 9(17), 3491; https://doi.org/10.3390/app9173491 - 23 Aug 2019

Cited by 13 | Viewed by 2634

Abstract

To improve the accuracy and efficiency of fault data identification of traffic detectors is crucial in order to decrease the probability of unexpected failures of the intelligent transportation system (ITS). Since convolutional fault data recognition based on traffic flow three-parameter law has a [...] Read more.

To improve the accuracy and efficiency of fault data identification of traffic detectors is crucial in order to decrease the probability of unexpected failures of the intelligent transportation system (ITS). Since convolutional fault data recognition based on traffic flow three-parameter law has a poor capability for multiscale of fault data, PCA (principal component analysis) is adopted for traffic fault data identification. This paper proposes the fault data detection models based on the PCA model, MSPCA (multiscale principal component analysis) model and improved MSPCA model, respectively. In order to improve the recognition rate of traffic detectors’ fault data, the improved MSPCA model combines the wavelet packet energy analysis and PCA to achieve traffic detector data fault identification. On the basis of traditional MSPCA, wavelet packet multi-scale decomposition is used to get detailed information, and principal component analysis models are established on different scale matrices, and fault data are separated by wavelet packet energy difference. Through case analysis, the feasibility verification of traffic flow data identification method is carried out. The results show that the improved method proposed in this paper is effective for identifying traffic fault data. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

21 pages, 5517 KiB

Open AccessArticle

A Speed Tracking Method for Autonomous Driving via ADRC with Extended State Observer

by Shuo Xiong, Hui Xie, Kang Song and Guohui Zhang

Appl. Sci. 2019, 9(16), 3339; https://doi.org/10.3390/app9163339 - 14 Aug 2019

Cited by 13 | Viewed by 2971

Abstract

This paper proposes the extended state observer (ESO)-based active disturbance rejection control (ADRC) for the speed tracking of an autonomous vehicle. Uncertainties, both in the vehicle plant and in the sensors, such as nonlinear uncertainties due to the powertrain dynamics, variations in rolling [...] Read more.

This paper proposes the extended state observer (ESO)-based active disturbance rejection control (ADRC) for the speed tracking of an autonomous vehicle. Uncertainties, both in the vehicle plant and in the sensors, such as nonlinear uncertainties due to the powertrain dynamics, variations in rolling resistance and air resistance, are all estimated in real-time by an extended state observer (ESO). Furthermore, a simple vehicle longitudinal dynamics model, including a mean value engine model (MVEM), is implemented to obtain the parameters in ADRC and design a feedforward controller to enhance the controller’s performance. The proposed controller is validated through CarSim^®/Simulink^® simulations and road tests. The simulation validates the adaptiveness of the proposed controller against the well-tuned proportional integral derivative (PID) controller, and the speed tracking error of the proposed controller is within 1.26% in simulation. Simulation results also show that fuel consumption can be improved by 3.6% by changing the accelerator pedal depth and positive rate. Finally, the road tests are completed under four kinds of road conditions, and the maximum tracking error is smaller than 0.5 km/h. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

Review

Jump to: Research, Other

19 pages, 1445 KiB

Open AccessReview

An Overview of Reinforcement Learning Methods for Variable Speed Limit Control

by Krešimir Kušić, Edouard Ivanjko, Martin Gregurić and Mladen Miletić

Appl. Sci. 2020, 10(14), 4917; https://doi.org/10.3390/app10144917 - 17 Jul 2020

Cited by 29 | Viewed by 5176

Abstract

Variable Speed Limit (VSL) control systems are widely studied as solutions for improving safety and throughput on urban motorways. Machine learning techniques, specifically Reinforcement Learning (RL) methods, are a promising alternative for setting up VSL since they can learn and react to different [...] Read more.

Variable Speed Limit (VSL) control systems are widely studied as solutions for improving safety and throughput on urban motorways. Machine learning techniques, specifically Reinforcement Learning (RL) methods, are a promising alternative for setting up VSL since they can learn and react to different traffic situations without knowing the explicit model of the motorway dynamics. However, the efficiency of combined RL-VSL is highly related to the class of the used RL algorithm, and description of the managed motorway section in which the RL-VSL agent sets the appropriate speed limits. Currently, there is no existing overview of RL algorithm applications in the domain of VSL. Therefore, a comprehensive survey on the state of the art of RL-VSL is presented. Best practices are summarized, and new viewpoints and future research directions, including an overview of current open research questions are presented. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

25 pages, 10337 KiB

Open AccessReview

Application of Deep Reinforcement Learning in Traffic Signal Control: An Overview and Impact of Open Traffic Data

by Martin Gregurić, Miroslav Vujić, Charalampos Alexopoulos and Mladen Miletić

Appl. Sci. 2020, 10(11), 4011; https://doi.org/10.3390/app10114011 - 10 Jun 2020

Cited by 62 | Viewed by 7681

Abstract

Persistent congestions which are varying in strength and duration in the dense traffic networks are the most prominent obstacle towards sustainable mobility. Those types of congestions cannot be adequately resolved by the traditional Adaptive Traffic Signal Control (ATSC). The introduction of Reinforcement Learning [...] Read more.

Persistent congestions which are varying in strength and duration in the dense traffic networks are the most prominent obstacle towards sustainable mobility. Those types of congestions cannot be adequately resolved by the traditional Adaptive Traffic Signal Control (ATSC). The introduction of Reinforcement Learning (RL) in ATSC as tackled those types of congestions by using on-line learning, which is based on the trial and error approach. Furthermore, RL is prone to the dimensionality curse related to the state–action space size based on which a non-linear quality function is derived. The Deep Reinforcement Learning (DRL) framework uses Deep Neural Networks (DNN) to digest raw traffic data to approximate the quality function of RL. This paper provides a comprehensive analysis of the most recent DRL approaches used for the ATSC algorithm design. Special emphasis is set to overview of the traffic state representation and multi-agent DRL frameworks applied for the large traffic networks. Best practices are provided for choosing the adequate DRL model, hyper-parameters tuning, and model architecture design. Finally, this paper provides a discussion about the importance of the open traffic data concept for the extensive application of DRL in the real world ATSC. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

Other

Jump to: Research, Review

9 pages, 1344 KiB

Open AccessLetter

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

by Donghoon Shin, Subin Yi, Kang-moon Park and Manbok Park

Appl. Sci. 2020, 10(6), 2138; https://doi.org/10.3390/app10062138 - 21 Mar 2020

Cited by 9 | Viewed by 2557

Abstract

Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries. Predicting turning, passing, and stop maneuvers against surrounding vehicles is considered to be fundamental for advanced driver assistance systems (ADAS), or automated driving systems in urban intersections. [...] Read more.

Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries. Predicting turning, passing, and stop maneuvers against surrounding vehicles is considered to be fundamental for advanced driver assistance systems (ADAS), or automated driving systems in urban intersections. In order to estimate the target intent in such situations, an interacting multiple model (IMM)-based intersection-target-intent estimation algorithm is proposed. A driver model is developed to represent the driver’s maneuvering on the intersection using an IMM-based target intent classification algorithm. The performance of the intersection-target-intent estimation algorithm is examined through simulation studies. It is demonstrated that the intention of a target vehicle is successfully predicted based on observations at an individual intersection by proposed algorithms. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Intelligent Transportation Systems

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (23 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI