2,370 research outputs found
Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions
Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research
Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions
Open access articleCurrently autonomous or self-driving vehicles are at the heart of academia and industry
research because of its multi-faceted advantages that includes improved safety, reduced congestion,lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature.
The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing
technologies that are essential for planning with the aim of reducing the total cost of
driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research
A bi-level model of dynamic traffic signal control with continuum approximation
This paper proposes a bi-level model for traffic network signal control, which is formulated as a dynamic Stackelberg game and solved as a mathematical program with equilibrium constraints (MPEC). The lower-level problem is a dynamic user equilibrium (DUE) with embedded dynamic network loading (DNL) sub-problem based on the LWR model (Lighthill and Whitham, 1955; Richards, 1956). The upper-level decision variables are (time-varying) signal green splits with the objective of minimizing network-wide travel cost. Unlike most existing literature which mainly use an on-and-off (binary) representation of the signal controls, we employ a continuum signal model recently proposed and analyzed in Han et al. (2014), which aims at describing and predicting the aggregate behavior that exists at signalized intersections without relying on distinct signal phases. Advantages of this continuum signal model include fewer integer variables, less restrictive constraints on the time steps, and higher decision resolution. It simplifies the modeling representation of large-scale urban traffic networks with the benefit of improved computational efficiency in simulation or optimization. We present, for the LWR-based DNL model that explicitly captures vehicle spillback, an in-depth study on the implementation of the continuum signal model, as its approximation accuracy depends on a number of factors and may deteriorate greatly under certain conditions. The proposed MPEC is solved on two test networks with three metaheuristic methods. Parallel computing is employed to significantly accelerate the solution procedure
๋์ฌ ๊ต์ฐจ๋ก์์์ ์์จ์ฃผํ์ ์ํ ์ฃผ๋ณ ์ฐจ๋ ๊ฒฝ๋ก ์์ธก ๋ฐ ๊ฑฐ๋ ๊ณํ ์๊ณ ๋ฆฌ์ฆ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ,2020. 2. ์ด๊ฒฝ์.์ฐจ๋์ฉ ์ผ์ฑ ๋ฐ ์ฒ๋ฆฌ๊ธฐ์ ์ด ๋ฐ๋ฌํจ์ ๋ฐ๋ผ ์๋์ฐจ ๊ธฐ์ ์ฐ๊ตฌ๊ฐ ์๋ ์์ ๊ธฐ์ ์์ ๋ฅ๋ ์์ ๊ธฐ์ ๋ก ์ด์ ์ด ํ์ฅ๋๊ณ ์๋ค. ์ต๊ทผ, ์ฃผ์ ์๋์ฐจ ์ ์์ฌ๋ค์ ๋ฅ๋ํ ์ฐจ๊ฐ๊ฑฐ๋ฆฌ ์ ์ด, ์ฐจ์ ์ ์ง ๋ณด์กฐ, ๊ทธ๋ฆฌ๊ณ ๊ธด๊ธ ์๋ ์ ๋๊ณผ ๊ฐ์ ๋ฅ๋ ์์ ๊ธฐ์ ์ด ์ด๋ฏธ ์์
ํํ๊ณ ์๋ค. ์ด๋ฌํ ๊ธฐ์ ์ ์ง๋ณด๋ ์ฌ์๋ฅ ์ ๋ก๋ฅผ ๋ฌ์ฑํ๊ธฐ ์ํ์ฌ ๊ธฐ์ ์ฐ๊ตฌ ๋ถ์ผ๋ฅผ ๋ฅ๋ ์์ ๊ธฐ์ ์ ๋์ด์ ์์จ์ฃผํ ์์คํ
์ผ๋ก ํ์ฅ์ํค๊ณ ์๋ค. ํนํ, ๋์ฌ ๋๋ก๋ ์ธ๋, ์ฌ๊ฐ์ง๋, ์ฃผ์ฐจ์ฐจ๋, ์ด๋ฅ์ฐจ, ๋ณดํ์ ๋ฑ๊ณผ ๊ฐ์ ๊ตํต ์ํ ์์๋ฅผ ๋ง์ด ๊ฐ๊ณ ์๊ธฐ ๋๋ฌธ์ ๊ณ ์๋๋ก๋ณด๋ค ์ฌ๊ณ ๋ฐ์๋ฅ ๊ณผ ์ฌ์๋ฅ ์ด ๋์ผ๋ฉฐ, ์ด๋ ๋์ฌ ๋๋ก์์์ ์์จ์ฃผํ์ ํต์ฌ ์ด์๊ฐ ๋๊ณ ์๋ค. ๋ง์ ํ๋ก์ ํธ๋ค์ด ์์จ์ฃผํ์ ํ๊ฒฝ์ , ์ธ๊ตฌํ์ , ์ฌํ์ , ๊ทธ๋ฆฌ๊ณ ๊ฒฝ์ ์ ์ธก๋ฉด์์์ ์์จ์ฃผํ์ ํจ๊ณผ๋ฅผ ํ๊ฐํ๊ธฐ ์ํด ์ํ๋์๊ฑฐ๋ ์ํ ์ค์ ์๋ค. ์๋ฅผ ๋ค์ด, ์ ๋ฝ์ AdaptIVE๋ ๋ค์ํ ์์จ์ฃผํ ๊ธฐ๋ฅ์ ๊ฐ๋ฐํ์์ผ๋ฉฐ, ๊ตฌ์ฒด์ ์ธ ํ๊ฐ ๋ฐฉ๋ฒ๋ก ์ ๊ฐ๋ฐํ์๋ค. ๋ํ, CityMobil2๋ ์ ๋ฝ ์ ์ญ์ 9๊ฐ์ ๋ค๋ฅธ ํ๊ฒฝ์์ ๋ฌด์ธ ์ง๋ฅํ ์ฐจ๋์ ์ฑ๊ณต์ ์ผ๋ก ํตํฉํ์๋ค. ์ผ๋ณธ์์๋ 2014๋
5์์ ์์๋ Automated Driving System Research Project๋ ์์จ์ฃผํ ์์คํ
๊ณผ ์ฐจ์ธ๋ ๋์ฌ ๊ตํต ์๋จ์ ๊ฐ๋ฐ ๋ฐ ๊ฒ์ฆ์ ์ด์ ์ ๋ง์ถ์๋ค.
๊ธฐ์กด ์ฐ๊ตฌ๋ค์ ๋ํ ์กฐ์ฌ๋ฅผ ํตํด ์์จ์ฃผํ ์์คํ
์ ๊ตํต ์ฐธ์ฌ์๋ค์ ์์ ๋๋ฅผ ํฅ์์ํค๊ณ , ๊ตํต ํผ์ก์ ๊ฐ์์ํค๋ฉฐ, ์ด์ ์ ํธ์์ฑ์ ์ฆ์ง์ํค๋ ๊ฒ์ด ์ฆ๋ช
๋์๋ค. ๋ค์ํ ๋ฐฉ๋ฒ๋ก ๋ค์ด ์ธ์ง, ๊ฑฐ๋ ๊ณํ, ๊ทธ๋ฆฌ๊ณ ์ ์ด์ ๊ฐ์ ๋์ฌ ๋๋ก ์์จ์ฃผํ์ฐจ์ ํต์ฌ ๊ธฐ์ ๋ค์ ๊ฐ๋ฐํ๊ธฐ ์ํ์ฌ ์ฌ์ฉ๋์๋ค. ํ์ง๋ง ๋ง์ ์ต์ ์ ์์จ์ฃผํ ์ฐ๊ตฌ๋ค์ ๊ฐ ๊ธฐ์ ์ ๊ฐ๋ฐ์ ๋ณ๊ฐ๋ก ๊ณ ๋ คํ์ฌ ์งํํด์๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก ํตํฉ์ ์ธ ๊ด์ ์์์ ์์จ์ฃผํ ๊ธฐ์ ์ค๊ณ๋ ์์ง ์ถฉ๋ถํ ๊ณ ๋ ค๋์ด ์์๋ค.
๋ฐ๋ผ์, ๋ณธ ๋
ผ๋ฌธ์ ๋ณต์กํ ๋์ฌ ๋๋ก ํ๊ฒฝ์์ ๋ผ์ด๋ค, ์นด๋ฉ๋ผ, GPS, ๊ทธ๋ฆฌ๊ณ ๊ฐ๋จํ ๊ฒฝ๋ก ๋งต์ ๊ธฐ๋ฐํ ์์ ์์จ์ฃผํ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ๋ฐํ๋ ๊ฒ์ ๋ชฉํ๋ก ํ์๋ค. ์ ์๋ ์์จ์ฃผํ ์๊ณ ๋ฆฌ์ฆ์ ๋นํต์ ๊ต์ฐจ๋ก๋ฅผ ํฌํจํ ๋์ฌ ๋๋ก ์ํฉ์ ์ฐจ๋ ๊ฑฐ๋ ์์ธก๊ธฐ์ ๋ชจ๋ธ ์์ธก ์ ์ด ๊ธฐ๋ฒ์ ๊ธฐ๋ฐํ์ฌ ์ค๊ณ๋์๋ค. ๋ณธ ๋
ผ๋ฌธ์ ๋์ , ์ ์ ํ๊ฒฝ ํํ ๋ฐ ์ข
ํก๋ฐฉํฅ ๊ฑฐ๋ ๊ณํ์ ์ค์ ์ ์ผ๋ก ๋ค๋ฃจ์๋ค.
๋ณธ ๋
ผ๋ฌธ์ ๋์ฌ ๋๋ก ์์จ์ฃผํ์ ์ํ ๊ฑฐ๋ ๊ณํ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ์๋ฅผ ์ ์ํ์์ผ๋ฉฐ, ์ค์ ๊ตํต ์ํฉ์์์ ์คํ ๊ฒฐ๊ณผ๋ ์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ํจ๊ณผ์ฑ๊ณผ ์ด์ ์ ๊ฑฐ๋๊ณผ์ ์ ์ฌ์ฑ์ ๋ณด์ฌ์ฃผ์๋ค. ์ค์ฐจ ์คํ ๊ฒฐ๊ณผ๋ ๋นํต์ ๊ต์ฐจ๋ก๋ฅผ ํฌํจํ ๋์ฌ ์๋๋ฆฌ์ค์์์ ๊ฐ๊ฑดํ ์ฑ๋ฅ์ ๋ณด์ฌ์ฃผ์๋ค.The foci of automotive researches have been expanding from passive safety systems to active safety systems with advances in sensing and processing technologies. Recently, the majority of automotive makers have already commercialized active safety systems, such as adaptive cruise control (ACC), lane keeping assistance (LKA), and autonomous emergency braking (AEB). Such advances have extended the research field beyond active safety systems to automated driving systems to achieve zero fatalities. Especially, automated driving on urban roads has become a key issue because urban roads possess numerous risk factors for traffic accidents, such as sidewalks, blind spots, on-street parking, motorcycles, and pedestrians, which cause higher accident rates and fatalities than motorways. Several projects have been conducted, and many others are still underway to evaluate the effects of automated driving in environmental, demographic, social, and economic aspects. For example, the European project AdaptIVe, develops various automated driving functions and defines specific evaluation methodologies. In addition, CityMobil2 successfully integrates driverless intelligent vehicles in nine other environments throughout Europe. In Japan, the Automated Driving System Research Project began on May 2014, which focuses on the development and verification of automated driving systems and next-generation urban transportation.
From a careful review of a considerable amount of literature, automated driving systems have been proven to increase the safety of traffic users, reduce traffic congestion, and improve driver convenience. Various methodologies have been employed to develop the core technology of automated vehicles on urban roads, such as perception, motion planning, and control. However, the current state-of-the-art automated driving algorithms focus on the development of each technology separately. Consequently, designing automated driving systems from an integrated perspective is not yet sufficiently considered.
Therefore, this dissertation focused on developing a fully autonomous driving algorithm in urban complex scenarios using LiDAR, vision, GPS, and a simple path map. The proposed autonomous driving algorithm covered the urban road scenarios with uncontrolled intersections based on vehicle motion prediction and model predictive control approach. Mainly, four research issues are considered: dynamic/static environment representation, and longitudinal/lateral motion planning.
In the remainder of this thesis, we will provide an overview of the proposed motion planning algorithm for urban autonomous driving and the experimental results in real traffic, which showed the effectiveness and human-like behaviors of the proposed algorithm. The proposed algorithm has been tested and evaluated using both simulation and vehicle tests. The test results show the robust performance of urban scenarios, including uncontrolled intersections.Chapter 1 Introduction 1
1.1. Background and Motivation 1
1.2. Previous Researches 4
1.3. Thesis Objectives 9
1.4. Thesis Outline 10
Chapter 2 Overview of Motion Planning for Automated Driving System 11
Chapter 3 Dynamic Environment Representation with Motion Prediction 15
3.1. Moving Object Classification 17
3.2. Vehicle State based Direct Motion Prediction 20
3.2.1. Data Collection Vehicle 22
3.2.2. Target Roads 23
3.2.3. Dataset Selection 24
3.2.4. Network Architecture 25
3.2.5. Input and Output Features 33
3.2.6. Encoder and Decoder 33
3.2.7. Sequence Length 34
3.3. Road Structure based Interactive Motion Prediction 36
3.3.1. Maneuver Definition 38
3.3.2. Network Architecture 39
3.3.3. Path Following Model based State Predictor 47
3.3.4. Estimation of predictor uncertainty 50
3.3.5. Motion Parameter Estimation 53
3.3.6. Interactive Maneuver Prediction 56
3.4. Intersection Approaching Vehicle Motion Prediction 59
3.4.1. Driver Behavior Model at Intersections 59
3.4.2. Intention Inference based State Prediction 63
Chapter 4 Static Environment Representation 67
4.1. Static Obstacle Map Construction 69
4.2. Free Space Boundary Decision 74
4.3. Drivable Corridor Decision 76
Chapter 5 Longitudinal Motion Planning 81
5.1. In-Lane Target Following 82
5.2. Proactive Motion Planning for Narrow Road Driving 85
5.2.1. Motivation for Collision Preventive Velocity Planning 85
5.2.2. Desired Acceleration Decision 86
5.3. Uncontrolled Intersection 90
5.3.1. Driving Phase and Mode Definition 91
5.3.2. State Machine for Driving Mode Decision 92
5.3.3. Motion Planner for Approach Mode 95
5.3.4. Motion Planner for Risk Management Phase 98
Chapter 6 Lateral Motion Planning 105
6.1. Vehicle Model 107
6.2. Cost Function and Constraints 109
Chapter 7 Performance Evaluation 115
7.1. Motion Prediction 115
7.1.1. Prediction Accuracy Analysis of Vehicle State based Direct Motion Predictor 115
7.1.2. Prediction Accuracy and Effect Analysis of Road Structure based Interactive Motion Predictor 122
7.2. Prediction based Distance Control at Urban Roads 132
7.2.1. Driving Data Analysis of Direct Motion Predictor Application at Urban Roads 133
7.2.2. Case Study of Vehicle Test at Urban Roads 138
7.2.3. Analysis of Vehicle Test Results on Urban Roads 147
7.3. Complex Urban Roads 153
7.3.1. Case Study of Vehicle Test at Complex Urban Roads 154
7.3.2. Closed-loop Simulation based Safety Analysis 162
7.4. Uncontrolled Intersections 164
7.4.1. Simulation based Algorithm Comparison of Motion Planner 164
7.4.2. Monte-Carlo Simulation based Safety Analysis 166
7.4.3. Vehicle Tests Results in Real Traffic Conditions 172
7.4.4. Similarity Analysis between Human and Automated Vehicle 194
7.5. Multi-Lane Turn Intersections 197
7.5.1. Case Study of a Multi-Lane Left Turn Scenario 197
7.5.2. Analysis of Motion Planning Application Results 203
Chapter 8 Conclusion & Future Works 207
8.1. Conclusion 207
8.2. Future Works 209
Bibliography 210
Abstract in Korean 219Docto
Enhancing Energy Efficiency in Connected Vehicles Via Access to Traffic Signal Information
This dissertation expounds on algorithms that can deterministically or proba-bilistically predict the future Signal Phase and Timing (SPAT) of a traffic signal by relying on real-time information from numerous vehicles and traffic infrastructure, historical data, and the computational power of a back-end computing cluster. When made available on an open server, predictive information about traffic signalsโ states can be extremely valuable in enabling new fuel efficiency and safety functionalities in connected vehicles: Predictive Cruise Control (PCC) can use the predicted timing plan to calculate globally optimal velocity trajectories that reduce idling time at red signals and therefore improve fuel e๏ฌciency and reduce emissions. Advanced engine management strategies can shut down the engine in anticipation of a long idling interval at red. Intersection collision avoidance is another functionality that can benefit from the prediction.
We start by exploring a globally optimal velocity planning algorithm through the use of Dynamic Programming (DP), and provide to it three levels of traffic signal information - none, real-time only, and full-future information. The no-information case represents the average driver today, and is expected to provide an energy efficiency minimum or baseline. The full-information case represents a driver with full and exact knowledge of the future red and green times of all the traffic signals along their route, and is expected to provide an energy efficiency maximum. We propose a probabilistic method that seeks to optimize fuel efficiency when only real-time only information is available with the goal of obtaining fuel efficiency as close to the full-future knowledge example as possible. We used Monte-Carlo simulations to evaluate whether the fuel efficiency gains found were merely the result of lucky case studies or whether they were statistically significant; we found in related case studies that up to 16% gains in fuel economy were possible. While these results were promising, the delivery of relevant and accurate future traffic signal phase and timing information remained an unsolved problem.
The next step we took was towards building The next step we took was towards building traffic signal prediction models. We took several prescient techniques from the data mining and machine learning ๏ฌelds, and adapted them to our purposes in the exploration of massive amounts of data recorded from traffic Management Centers (TMCs). This manuscript evaluates Transition Probability Modeling, Decision Tree, Multi-Linear Regression, and Neural Network machine learning methods for use in the prediction of traffic Signal Phase and Timing (SPaT) information. signal prediction models. We took several prescient techniques from the data mining and machine learning ๏ฌelds, and adapted them to our purposes in the exploration of massive amounts of data recorded from traffic Management Centers (TMCs). This manuscript evaluates Transition Probability Modeling, Decision Tree, Multi-Linear Regression, and Neural Network machine learning methods for use in the prediction of traffic Signal Phase and Timing (SPaT) information.
Finally, we evaluated the in๏ฌuence of providing SPaT data to vehicles. To that end, we investigated both smartphone and in-vehicle proof-of-concepts. An in-vehicle velocity recommendation application has been tested in two cities: San Jose, California and San Francisco, California. The two test locations used two different data sources: data directly from a TMC, and data crowdsourced from public transit bus routes, respectively. A total of 14 test drivers were used to evaluate the effectiveness of the algorithm. In San Jose, the algorithm was found to produce a 8.4% improvement in fuel economy. In San Francisco, traffic conditions were not conducive to testing as the driver was unable to signi๏ฌcantly vary his speed to follow the recommendation algorithm, and a negligible difference in fuel economy was observed. However, it did provide an opportunity to evaluate the quality of data coming from the crowdsourced data algorithms. Predicted phase timing com-pared to camera-recorded ground truth data indicated an RMS difference (error) in prediction of approximately 4.1 seconds
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An operational method for assessing traffic-related air pollution in urban streets
Urban air quality has been a topic of major public concern and scientific research in recent years. Several theoretical and experimental studies have focused on the assessment of air quality within street canyons and other microenvironments (intersections, motorways, parking spaces, etc.), where population exposure to traffic-related pollutants is relatively high.
The aim of this study was to develop a practical methodology for assessing traffic-related air pollution in urban streets, after testing available monitoring and modelling techniques. To meet this objective, a large amount of original air quality, meteorological and traffic data were collected during four intensive short-term and one long-term monitoring campaigns carried out in the region of Paris from December 1998 to December 2001. These campaigns covered three representative street canyon sites (Bd. Voltaire, Rue de Rennes, Av. Leclerc - PI. Basch) as well as a motorway service station (RN10 petrol station).
Passive and active monitoring techniques were used to sample a wide range of inorganic (CO, NO X and Os) and organic gases (benzene, toluene, xylene, ethylbenzene, formaldehyde, acetaldehyde, etc.) at different heights and distances from the kerb. Indicative background measurements were also taken during the same sampling periods. Furthermore, relevant meteorological (synoptic and local) and traffic information was obtained on each site.
The analysis of the data gave insights into the dispersion and transformation processes taking place within the streets. Channelling effects induced by parallel to the road axis winds gave rise to relatively high kerbside pollution levels. On the other hand, perpendicular synoptic winds generated air vortices within the canyons, which resulted in steep crossroad concentration gradients. In that case, higher pollution levels were observed on the leeward than on the windward side of the streets. A significant reduction of concentrations with height above the ground was also observed within two of the street canyons (Bd. Voltaire and Av. Leclerc). In all cases, roadside concentrations were several times higher than the corresponding urban background values.
This spatial variability indicates a strong transport effect on the pollutant distribution within urban canyons, caused by the synoptic wind and influenced by the geometry of the street. That may have serious implications in terms of population exposure and compliance with air quality legislation. In this context, the siting of permanent monitoring equipment becomes crucial.
A relationship between CO and benzene as well as an exponential expression linking pollutant concentrations at different heights within the canyons were empirically deduced. Five dispersion models of different levels of complexity (STREET-SRI, OSPM, AEOLIUS, CAR-International, and CALINE4) were used to calculate CO and benzene concentrations at the campaign sites. The Computational Fluid Dynamic code PHOENICS was also tested for one location.
The comparison between observed and predicted values revealed the advantages and drawbacks of each model in association with the configuration of the street and the meteorological conditions. Furthermore, a sensitivity and uncertainty analysis involving three of the available models (STREET-SRI, OSPM and AEOLIUS) was carried out. OSPM was slightly modified in order to allow user access to certain internally coded parameters.
An operational method combining multi-site sampling and dispersion modelling was finally proposed for assessing air quality in urban streets, taking into account the pronounced spatial and temporal variability of traffic-related air pollution, the modelling uncertainty, the practical constraints related to measurements and models, and the needs of decision makers. This methodology may find wider application in air quality management, urban and transport planning, and population exposure studies
Evaluation of the Driving Performance and User Acceptance of a Predictive Eco-Driving Assistance System for Electric Vehicles
In this work, a predictive eco-driving assistance system (pEDAS) with the
goal to assist drivers in improving their driving style and thereby reducing
the energy consumption in battery electric vehicles while enhancing the driving
safety and comfort is introduced and evaluated. pEDAS in this work is equipped
with two model predictive controllers (MPCs), namely reference-tracking MPC and
car-following MPC, that use the information from onboard sensors, signal phase
and timing (SPaT) messages from traffic light infrastructure, and geographical
information of the driving route to compute an energy-optimal driving speed. An
optimal speed suggestion and informative advice are indicated to the driver
using a visual feedback. pEDAS provides continuous feedback and encourages the
drivers to perform energy-efficient car-following while tracking a preceding
vehicle, travel at safe speeds at turns and curved roads, drive at
energy-optimal speed determined using dynamic programming in freeway scenarios,
and travel with a green-wave optimal speed to cross the signalized
intersections at a green phase whenever possible. Furthermore, to evaluate the
efficacy of the proposed pEDAS, user studies were conducted with 41
participants on a dynamic driving simulator. The objective analysis revealed
that the drivers achieved mean energy savings up to 10%, reduced the speed
limit violations, and avoided unnecessary stops at signalized intersections by
using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated
using the Technology Acceptance Model (TAM) and Theory of Planned Behavior
(TPB). The results showed an overall positive attitude of users and that the
perceived usefulness and perceived behavioral control were found to be the
significant factors in influencing the behavioral intention to use pEDAS.Comment: Submitted to Transportation Research Part C: Emerging Technologies
Journa
Towards a real-time microscopic emissions model
This article presents a new approach to microscopic road traffic exhaust emission modelling. The model described uses data from the SCOOT demand-responsive traffic control system implemented in over 170 cities across the world. Estimates of vehicle speed and classification are made using data from inductive detector loops located on every SCOOT link. This data feeds into a microscopic traffic model to enable enhanced modelling of the driving modes of vehicles (acceleration, deceleration, idling and cruising). Estimates of carbon monoxide emissions are made
by applying emission factors from an extensive literature review. A critical appraisal of the development and validation of the model is given before the model is applied to a study of the impact of high emitting vehicles. The article concludes with a discussion of the requirements for the future development and benefits of the
application of such a model
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