Vehicle Based Intersection Management with Intelligent Agents

Signal-based intersection management will change when vehicles with intelligent capability are available in the future. Intelligent agents embedded in vehicle software will be responsible for vehicle control and route guidance. Intersection management can be achieved through the collaboration of these agents, without a centralized control infrastructure. This research focuses on the use of distributed multi-agent systems to provide microscopic adaptive control which might reduce traffic delay and chances of collisions at intersections. A hypothesized Mobile Ad-hoc Network provides communication links to connect the agents.Intelligent Agents, Adaptive Intersection Control

Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

Modeling Pipeline Driving Behaviors: A Hidden Markov Model Approach

Driving behaviors at intersection are complex because drivers have to perceive more traffic events than normal road driving and thus are exposed to more errors with safety consequences. Drivers make real-time responsesin a stochastic manner. This paper presents our study using Hidden Markov Models (HMM) to model driving behaviors at intersections. Observed vehicle movement data are used to build up the model. A single HMM is used to cluster the vehicle movements when they are close to intersection. The re-estimated clustered HMMs provide better prediction of the vehicle movements compared to traditional car-following models. Only through vehicles on major roads are considered in this paper.

Analysis of Pedestrian Safety Using Micro-simulation and Driving Simulator

In recent years, traffic agencies have begun to place emphasis on the importance of pedestrian safety. In the United States, nearly 70,000 pedestrians were reported injured in 2015. Although the number only account for 3% of all the people injured in traffic crashes, the number of pedestrian fatalities is still around 15% of total traffic fatalities. Furthermore, the state of Florida has consistently ranked as one of the worst states in terms of pedestrian crashes, injuries and fatalities. Therefore, it is befitting to focus on the pedestrian safety. This dissertation mainly focused on pedestrian safety at both midblock crossings and intersections by using micro-simulation and driving simulator. First, this study examined if the micro-simulation models (VISSIM and SSAM) could estimate pedestrian-vehicle conflicts at signalized intersections. A total of 42 video-hours were recorded at seven signalized intersections for field data collection. The observed conflicts from the field were used to calibrate VISSIM and replicate the conflicts. The calibrated and validated VISSIM model generated the pedestrian-vehicle conflicts from SSAM software using the vehicle trajectory data in VISSIM. The mean absolute percent error (MAPE) was used to determine the optimum TTC and PET thresholds for pedestrian-vehicle conflicts and linear regression analysis was used to study the correlation between the observed and simulated conflicts at the established thresholds. The results indicated the highest correlation between the simulated and observed conflicts when the TTC parameter was set at 2.7 and the PET was set at 8. Second, the driving simulator experiment was designed to assess pedestrian safety under different potential risk factors at both midblock crossings and intersections. Four potential risk factors were selected and 67 subjects participated in this experiment. In order to analyze pedestrian safety, the surrogate safety measures were examined to evaluate these pedestrian-vehicle conflicts. Third, by using the driving simulator data from the midblock crossing scenario, typical examples of drivers\u27 deceleration rate and the distance to crosswalk were summarized, which exhibited a clear drivers\u27 avoidance pattern during the vehicle pedestrian conflicts. This pattern was summarized into four stages, including the brake response stage, the deceleration adjustment stage, the maximum deceleration stage, and the brake release stage. In addition, the pedestrian-vehicle conflict prediction model was built to predict the minimum distance between vehicle and pedestrian. Finally, this study summarized the three different kinds of data that were to evaluate the pedestrian safety, including field data, simulation data, and driving simulator data. The process of combining of field data, simulation data, and simulator data was proposed. The process would show how the researches could evaluate the pedestrian safety by using the field observations, micro-simulation, and driving simulator

Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

Integrating driving and traffic simulators for the study of railway level crossing safety interventions: a methodology

Safety at Railway Level Crossings (RLXs) is an important issue within the Australian transport system. Crashes at RLXs involving road vehicles in Australia are estimated to cost $10 million each year. Such crashes are mainly due to human factors; unintentional errors contribute to 46% of all fatal collisions and are far more common than deliberate violations. This suggests that innovative intervention targeting drivers are particularly promising to improve RLX safety. In recent years there has been a rapid development of a variety of affordable technologies which can be used to increase driver’s risk awareness around crossings. To date, no research has evaluated the potential effects of such technologies at RLXs in terms of safety, traffic and acceptance of the technology. Integrating driving and traffic simulations is a safe and affordable approach for evaluating these effects. This methodology will be implemented in a driving simulator, where we recreated realistic driving scenario with typical road environments and realistic traffic. This paper presents a methodology for evaluating comprehensively potential benefits and negative effects of such interventions: this methodology evaluates driver awareness at RLXs , driver distraction and workload when using the technology . Subjective assessment on perceived usefulness and ease of use of the technology is obtained from standard questionnaires. Driving simulation will provide a model of driving behaviour at RLXs which will be used to estimate the effects of such new technology on a road network featuring RLX for different market penetrations using a traffic simulation. This methodology can assist in evaluating future safety interventions at RLXs

Analyzing Crash Potential in Mixed Traffic with Autonomous and Human-Driven Vehicles

Reducing crash counts on saturated road networks is one of the most significant benefits behind the introduction of Autonomous Vehicle (AV) technology. To date, many researchers have studied how AVs maneuver in different traffic situations, but less attention has been paid to the car-following scenarios between AVs and human drivers. A mismatch in the braking and accelerating decisions in this car-following scenario can lead to rear-end near-crashes and therefore needs to be studied. This thesis aims to investigate the driving behavior of human-drivers that follow a designated AV leader in a car-following situation and compare the results with a scenario when the leader is a human-like driver. In this study, speed trajectory data was collected from 48 participants using a driving simulator. To estimate the near-crash risk between the participants and the leading vehicle, critical thresholds of six Surrogate Safety Measures (SSMs): Time to Collision (TTC), Inverse Time to Collision (ITTC), Modified Time to Collision (MTTC), Deceleration Rate to Avoid Crash (DRAC), critical jerk and Warning Index (WI), were used. The potential near-crash events and the safe driving events were classified using a random forest algorithm after performing oversampling and undersampling techniques. The results from the two-sample t-tests indicated a significant difference between the overall deceleration rates, braking speeds, and acceleration rates of the participants and the designated AV leader. However, no such difference was found between the participants and the human-like leader while braking and accelerating at stop-controlled intersections. Out of six SSMs, MTTC detected near-crash events 10 seconds before their actual occurrence at a range of 11.93 m with 83% accuracy. The surrogate measures identified a higher number of near-crash (high risk) events when the participants followed the designated AV and made braking maneuvers at the stop-controlled intersections. Based on the number of near-crash (high risk) events, the designated AV's C3.25 speed profile (with the maximum deceleration rate of 3.25 m/s2 ) posed the highest crash risk to the participants in the following vehicle. For potential near-crash events classification, a random forest classifier based on undersampled data achieved the highest average accuracy rate of 92.2%. The deceleration rates of the designated AV had the highest impact on the near-crashes between the AV and the participants. However, shorter clearances during the braking maneuvers at intersections significantly affected the near-crashes between the human-like leader and the participants in the following vehicle