Methodological Frontier in Operational Analysis for Roundabouts: A Review

Several studies and researches have shown that modern roundabouts are safe and effective as engineering countermeasures for traffic calming, and they are now widely used worldwide. The increasing use of roundabouts and, more recently, turbo and flower roundabouts, has induced a great variety of experiences in the field of intersection design, traffic safety, and capacity modeling. As for unsignalized intersections, which represent the starting point to extend knowledge about the operational analysis to roundabouts, the general situation in capacity estimation is still characterized by the discussion between gap acceptance models and empirical regression models. However, capacity modeling must contain both the analytical construction and then solution of the model, and the implementation of driver behavior. Thus, issues on a realistic modeling of driver behavior by the parameters that are included into the models are always of interest for practitioners and analysts in transportation and road infrastructure engineering. Based on these considerations, this paper presents a literature review about the key methodological issues in the operational analysis of modern roundabouts. Focus is made on the aspects associated with the gap acceptance behavior, the derivation of the analytical-based models, and the calculation of parameters included into the capacity equations, as well as steady-state and non-steady-state conditions and uncertainty in entry capacity estimation. At last, insights on future developments of the research in this field of investigation will be also outlined

Ecological Control and Coordination of Connected and Automated PHEVs at Roundabouts under Uncertainty

During the last decade, comprehensive research efforts were concentrated on autonomous driving. Annually, many car accidents happen as a result of human faults. Extreme traffic congestion prolongs commute time, increase air pollution and cause other transportation inefficiencies. Consequently, using advanced technologies to make vehicles less dependent on human drivers enable more efficient use of time for passengers and decrease car accidents. Connectivity between vehicles and automation provides a spectacular opportunity to improve traffic flow, safety, and efficiency. There are different main active research subjects under the broad domain of autonomous driving, one of them is intersection control for connected and automated vehicles (CAVs) which can be categorized into centralized and decentralized approaches. The environmental and strict regulatory demands require automotive companies to reduce Carbon Dioxide emissions by investing more in Electric Vehicles (EVs) and Plug-in Hybrid Electric vehicles (PHEVs). A PHEV equipped with connectivity and automation looks more interesting to automobile consumers since they can have advantages of both fewer emissions and enhanced abilities. Since the powertrain of PHEVs consists of different sources of power, advanced control techniques such as Model Predictive Control (MPC) is needed. Coordination of vehicles at roundabouts is a demanding problem especially by knowing that the chance of both lateral and longitudinal collision exists. To this end, first, we proposed a centralized nonlinear MPC-based controller to adhere to calculated priorities for connected and automated PHEVs (CA-PHEVs). We further continued this research by proposing an approach for solving nonlinear multi-objective optimal control problem of decentralized coordination of CA-PHEVs at roundabouts with consideration of fuel economy. It was found that the proposed controller can calculate priority based on a navigation function and provide a safe gap between vehicles. A novel priority calculation logic based on optimal control is proposed as well and its performance is compared with the navigation function approach. In addition to the decentralized control approach, we considered a more realistic robust tube-based nonlinear MPC decentralized approach to solve this problem in the presence of uncertainties. We used simulations to test the controller and a Toyota Prius PHEV high-fidelity model is used in this thesis for simulations. Simulation results show that the addition of robustness, and energy economy to performance index can improve the fuel consumption of the vehicle. One of the major concerns in designing a controller for automotive applications is real-time implementation. The results of hardware-in-the-loop experiments show the real-time implementation of the controllers

Interactive Prediction and Planning for Autonomous Driving: from Algorithms to Fundamental Aspects

Inevitably, autonomous vehicles need to interact with other road participants in a variety of highly complex or critical driving scenarios. It is still an extremely challenging task even for the forefront companies or institutes to enable autonomous vehicles to interactively predict the behavior of others, and plan safe and high-quality motions accordingly. The major obstacles are not just originated from prediction and planning algorithms with insufficient performances. Several fundamental problems in the fields of interactive prediction and planning still remain open, such as formulation, representation and evaluation of interactive prediction methods, motion dataset with densely interactive driving behavior, as well as interface of interactive prediction and planning algorithms. The aforementioned fundamental aspects of interactive prediction and planning are addressed in this dissertation along with various kinds of algorithms. First, generic environmental representation for various scenarios with topological decomposition is constructed, and a corresponding planning algorithm is designed by combining graph search and optimization. Hard constraints in optimization-based planners are also incorporated into the training loss of imitation learning so that the policy net can generate safe and feasible motions in highly constrained scenarios. Unified problem formulation and motion representation are designed for different paradigms of interactive predictors such as planning-based prediction (inverse reinforcement learning), as well as probabilistic graphical models (hidden Markov model) and deep neural networks (mixture density network), which are utilized for the prediction/planning interface design and prediction benchmark. A framework combing decision network and graph-search/optimization/sample-based planner is proposed to achieve a driving strategy which is defensive to potential violations of others, but not overly conservatively to threats of low probabilities. Such driving strategy is achieved via experiments based on the aforementioned interactive prediction and planning algorithms with proper interface designed. These predictors are also evaluated from closed loop perspective considering planning fatality when using the prediction results instead of pure data approximation metrics. Finally, INTERACTION (INTERnational, Adversarial and Cooperative moTION) dataset with highly interactive driving scenarios and behavior from international locations is constructed with interaction density metric defined to compare different datasets. The dataset has been utilized for various behavior-related research areas such as prediction, planning, imitation learning and behavior modeling, and is inspiring new research fields such as representation learning, interaction extraction and scenario generation

Adapting the Virtual Platooning Concept to Roundabout Crossing

International audienc

Driver Behaviour and Road Safety Analysis Using Computer Vision and Applications in Roundabout Safety

RÉSUMÉ L’un des principaux défis provenant de l’analyse traditionnelle de la sécurité routière basée sur les données historiques d’accidents est le besoin d’observer de véritables collisions entre les usagers de la route. Non seulement indésirables, ces collisions sont difficiles à observer. À cet effet, les méthodes d’analyse substitutive de la sécurité routière gagnent du terrain dans le milieu de la recherche en tant qu’alternative proactive à l’observation de ces accidents de la route : cette approche promet de modéliser indirectement les collisions par l’intermédiaire de précurseurs de collisions pouvant être retrouvés dans des données de circulation ordinaire : situations de trajectoire de collision, quasi-accidents, conflits de circulation, etc. En sus, l’analyse substitutive de la sécurité donne également les chercheurs un aperçu des mécanismes de collision, ce qui permettrait de mieux comprendre les facteurs favorisant les accidents. Cependant, un grand nombre de définition de ces situations précurseures de collision, ainsi que des problèmes de cohérence et de subjectivité des méthodes de collecte de données ont entravé l’adoption des méthodes d’analyse substitutive de la sécurité routière.----------ABSTRACT One of the main challenges of traditional road safety analysis based on historical accident records is its dependence on the occurrence and subsequent observation of real traffic collisions. Traffic collisions are not only undesirable, they are difficult to observe. Surrogate safety analysis is gaining traction in the research community as a proactive alternative to observing historical accident records. With this approach, collisions are instead predicted indirectly via precursors to collisions found in everyday traffic scenarios: collision-courses, near-misses, traffic conflicts, etc. Furthermore, the scope of surrogate safety analysis provides insight into collision mechanisms, allowing for better investigative procedures. However, the wide range of collision precursor definitions, and issues with inconsistent or sometimes subjective data collection methods have hampered surrogate safety analysis adoption

Autonomous navigation in interaction-based environments - a case of non-signalised roundabouts

To reduce the number of collision fatalities at crossroads intersections many countries have started replacing intersections with non-signalised roundabouts, forcing the drivers to be more situationally aware and to adapt their behaviours according to the scenario. A non-signalised roundabout adds to the autonomous vehicle planning challenge, as navigating such interaction dependent scenarios safely, efficiently and comfortably has been a challenge even for human drivers. Unlike traffic signal controlled roundabouts where the merging order is centrally controlled, driving a non-signalised roundabout requires the individual actor to make the decision to merge based on the movement of other interacting actors. Most traditional autonomous planning approaches use rule-based speed assignment for generating admissible motion trajectories, which work successfully in non-interaction-based driving scenarios. They, however, are less effective in interaction-based scenarios as they lack the necessary ability to adapt the vehicle's motion according to the evolving driving scenario. In this paper, we demonstrate an Adaptive Tactical Behaviour Planner (ATBP) for an autonomous vehicle that is capable of planning human-like motion behaviours for navigating a non-signalised roundabout, combining naturalistic behaviour planning and tactical decision-making algorithm. The human driving simulator experiment used to learn the behaviour planning approach and ATBP design are described in the paper