Simulation-based impact projection of autonomous vehicle deployment using real traffic flow

In this work we focus on future projected impacts of the autonomous vehicles in a realistic condition representing mixed traffic. By using real flow and speed data collected in 2002 and 2019 in the city of Gothenburg, we replicated and simulated the daily flow variation in SUMO. The expansion of the city in recent years was reflected in an increase in road users, and it is reasonable to expect it will increase further. Through simulations, it was possible to project this increase and to predict how this will impact the traffic in future. Furthermore, the composition of vehicle types in the future traffic can be expected to change through the introduction of autonomous vehicles. In order to predict the most likely drawbacks during the transition from a traffic consisting only manually driven vehicles to a traffic consisting only fully-autonomous vehicles, we focus on mixed traffic with different percentages of autonomous and manually driven vehicles. To realize this aim, several parameters of the car following and lane change models of autonomous vehicles are investigated in this paper. Along with the fundamental diagram, the number of lane changes and the number of conflicts are analyzed and studied as measures for improving road safety and efficiency

Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden

Understanding car-following behavior in different countries is essential for the design and development of autonomous driving and further development of active safety systems that can function well worldwide, in particular, in mixed traffic conditions. However, very few studies exist that compare car-following behaviors in different countries based on real driving data. This paper analyses the similarities and differences of drivers’ car-following behavior, in terms of time gap, gap distance, and time to collision (TTC), using both China and Sweden datasets from real road driving studies, in a bid to identify how these indicators affect drivers’ speed control in car-following situations. The results indicate that the highest frequency of gap distance is observed in the same value range in both datasets, while the highest frequency of time gap in the Sweden dataset is found at a lower value range than the corresponding value range in the China dataset. For both datasets, time gap is observed to be a more reliable indicator for car-following analysis than gap distance since it is less sensitive to speed variations. Furthermore, TTC in the low travel speed ranges (v < 50 km/h) tends to be steady in comparison with the TTC at other speed ranges, so the time gap in the high-speed ranges is (v > 90 km/h). Therefore, time gap is recommended as the safety indicator for car-following analysis in high-speed conditions, while a combination of time gap and TTC is recommended for low-speed conditions, especially on urban roads

Cooperative merging strategy between connected autonomous vehicles in mixed traffic

In this work we propose a new cooperation strategy between connected autonomous vehicles in on-ramps merging scenarios and we implement the cut-in risk indicator (CRI) to investigate the safety effect of the proposed strategy. The new cooperation strategy considers a pair of vehicles approaching an on-ramp. The strategy then makes decisions on the target speeds/accelerations of both vehicles, possible lane changing, and a dynamic decision-making approach in order to reduce the risk during the cut-in manoeuvre. In this work, the CRI was first used to assess the risk during the merging manoeuvre. For this purpose, scenarios with penetration rates of autonomous vehicles from 20% to 100%, with step of 10%, both connected and non-connected autonomous vehicles were evaluated. As a result, on average a 35% reduction of the cut-in risk manoeuvres in connected autonomous vehicles compared to non-connected autonomous vehicles is obtained. It is shown through the analysis of probability density functions characterising the CRI distribution that the reduction is not homogeneous across all indicator values, but depends on the penetration rate and the severity of the manoeuvre

Behavioral adaptation of drivers when driving among automated vehicles

PurposeThis paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles (AVs) compared to driving among manually driven vehicles (MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations. Here, mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs.Design/methodology/approachA driving simulator study is designed to explore whether such behavioral adaptations exist. Two different driving scenarios were explored on a three-lane highway: driving on the main highway and merging from an on-ramp. For this study, 18 research participants were recruited.FindingsBehavioral adaptation can be observed in terms of car-following speed, car-following time gap, number of lane change and overall driving speed. The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs. Although significant differences in behavior were found in more than 90% of the research participants, they adapted their behavior differently, and thus, magnitude of the behavioral adaptation remains unclear.Originality/valueThe observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles. This finding differs from previous studies, which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles. Furthermore, the surrounding vehicles in this study are more “free flow\u27” compared to previous studies with a fixed formation such as platoons. Nevertheless, long-term observations are required to further support this claim

Cooperative merging strategy between connected autonomous vehicles in mixed traffic

In this work we propose a new cooperation strategy between connected autonomous vehicles in on-ramps merging scenarios and we implement the cut-in risk indicator (CRI) to investigate the safety effect of the proposed strategy. The new cooperation strategy considers a pair of vehicles approaching an on-ramp. The strategy then makes decisions on the target speeds/accelerations of both vehicles, possible lane changing, and a dynamic decision-making approach in order to reduce the risk during the cut-in manoeuvre. In this work, the CRI was first used to assess the risk during the merging manoeuvre. For this purpose, scenarios with penetration rates of autonomous vehicles from 20% to 100%, with step of 10%, both connected and non-connected autonomous vehicles were evaluated. As a result, on average a 35% reduction of the cut-in risk manoeuvres in connected autonomous vehicles compared to non-connected autonomous vehicles is obtained. It is shown through the analysis of probability density functions characterising the CRI distribution that the reduction is not homogeneous across all indicator values, but depends on the penetration rate and the severity of the manoeuvre

LAPORAN KEGIATAN PRAKTIK LAPANGAN TERBIMBING (PLT)

Praktik Lapangan Terbimbing (PLT) merupakan salah satu bentuk mata kuliah yang bertujuan mendapatkan pengalaman tentang proses pembelajaran dan kegiatan persekolahan lainnya yang digunakan sebagai bekal untuk menjadi calon tenaga pendidik. Praktik Latihan Terbimbing (PLT) memiliki misi yaitu untuk menyiapkan dan menghasilkan tenaga kependidikan (guru) yang memiliki nilai, sikap, pengetahuan dan keterampilan secara profesional, maka pelaksanaan PLT ini akan sangat membantu mahasiswa dalam memasuki dunia kependidikan dan sebagai sarana untuk menerapkan ilmu yang diperolehnya selama mengikuti perkuliahan. Salah satu tempat yang menjadi lokasi PLT UNY 2017 adalah SMA Negeri 1 Pleret yang beralamat di Kedaton, Pleret, Pleret, Bantul, Yogyakarta. Kegiatan PLT dilaksanakan pada tanggal 15 September - 15 November 2017. Pelaksanaan kegiatan PLT dimulai dari observasi hingga pelaksanaan PLT yang terbagi menjadi beberapa tahap yaitu persiapan mengajar, pelaksanaan mengajar, dan evaluasi hasil mengajar. Kegiatan persiapan meliputi observasi pembelajaran di kelas yang dilakukan pada saat proses pembelajaran di kelas berlangsung dan pembuatan persiapan mengajar yaitu membuat satuan program materi pembelajaran yang berfungsi sebagai acuan dalam melaksanakan PBM di kelas agar berjalan lebih efektif dan efisien. Kegiatan praktik mengajar dilaksanakan di kelas XI IPA 1, XI IPA 2, XI IPA 3, XI IPA 4, XI IPS 1, XI IPS 2 Dan XI IPS 3. Hasil dari kegiatan PLT yaitu praktik mengajar sebanyak 35 kali tatap muka. Keberhasilan PLT yang dilakukan praktikan tidak terlepas dari peran praktikan yang didukung oleh pihak sekolah, khususnya guru pembimbing serta dosen pembimbing lapangan (DPL). Oleh karena itu, harapannya program kegiatan PLT dapat dijadikan sebagai wahana untuk pembentukan calon guru atau tenaga kependidikan yang professional

Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations

This paper introduces a unified mathematical definition for describing commonly used terms encountered in systematical analysis of automated driving systems in mixed-traffic simulations. The most significant contribution of this work is in translating the terms that are clarified previously in literature into a mathematical set and function based format. Our work can be seen as an incremental step towards further formalisation of Domain-Specific-Language (DSL) for scenario representation. We also extended the previous work in the literature to allow more complex scenarios by expanding the model-incompliant information using set-theory to represent the perception capacity of the road-user agents. With this dynamic perception definition, we also support interactive scenarios and are not limited to reactive and pre-defined agent behavior. Our main focus is to give a framework to represent realistic road-user behavior to be used in simulation or computational tool to examine interaction patterns in mixed-traffic conditions. We believe that, by formalising the verbose definitions and extending the previous work in DSL, we can support automatic scenario generation and dynamic/evolving agent behavior models for simulating mixed traffic situations and scenarios. In addition, we can obtain scenarios that are realistic but also can represent rare-conditions that are difficult to extract from field-tests and real driving data repositories

Potential impact of autonomous vehicles in mixed traffic from simulation using real traffic flow

This work focuses on the potential impacts of the autonomous vehicles in a mixed traffic condition represented in traffic simulator Simulation of Urban MObility (SUMO) with real traffic flow. Specifically, real traffic flow and speed data collected in 2002 and 2019 in Gothenburg were used to simulate daily flow variation in SUMO. In order to predict the most likely drawbacks during the transition from a traffic consisting only manually driven vehicles to a traffic consisting only fully-autonomous vehicles, this study focuses on mixed traffic with different percentages of autonomous and manually driven vehicles. To realize this aim, several parameters of the car following and lane change models of autonomous vehicles are investigated in this paper. Along with the fundamental diagram, the number of lane changes and the number of conflicts are analyzed and studied as measures for improving road safety and efficiency. The study highlights that the autonomous vehicles\u27 features that improve safety and efficiency in 100% autonomous and mixed traffic are different, and the ability of autonomous vehicles to switch between mixed and autonomous driving styles, and vice versa depending on the scenario, is necessary