Navigating roundabouts and unprotected turns in autonomous driving

© 2024 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TFR.2024.3421389The development of a fully autonomous driving vehicle (AV) requires various traffic situations to be handled efficiently. One of the most common driving manoeuvres which an AV experiences in daily traffic is giving way (yielding) to other traffic participants. In this paper, we propose a simple yet efficient method of yielding that doesn’t query yielding areas of interests from map API making it hassle free to use without having to rely on digitized yielding areas. We incorporated our method into one of the well-known open-source autonomy stacks called Autoware. The proposed method makes use of high-definition (HD) map elements including lanes and stoplines for filtering vehicles which participate in yielding decision making. Our method estimates future collisions of filtered vehicles of interest with AV’s planned trajectory and outputs a binary yielding decision for ego vehicle. Our method covers different yielding areas including a roundabout and an unprotected turn. We tested and evaluated the decision making of our method on various simulated scenarios and afterwards successful real-world tests were conducted using an in-house AV. An in-depth analysis of our approach shows that the proposed yielding solution works reasonably well i.e. 87% successful yielding area navigation ratio on real data.Peer reviewe

An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles

Due to the complexity of the natural world, a programmer cannot foresee all possible situations, a connected and autonomous vehicle (CAV) will face during its operation, and hence, CAVs will need to learn to make decisions autonomously. Due to the sensing of its surroundings and information exchanged with other vehicles and road infrastructure, a CAV will have access to large amounts of useful data. While different control algorithms have been proposed for CAVs, the benefits brought about by connectedness of autonomous vehicles to other vehicles and to the infrastructure, and its implications on policy learning has not been investigated in literature. This paper investigates a data driven driving policy learning framework through an agent-based modelling approaches. The contributions of the paper are two-fold. A dynamic programming framework is proposed for in-vehicle policy learning with and without connectivity to neighboring vehicles. The simulation results indicate that while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V) communication of information improves this capability. Furthermore, to overcome the limitations of sensing in a CAV, the paper proposes a novel concept for infrastructure-led policy learning and communication with autonomous vehicles. In infrastructure-led policy learning, road-side infrastructure senses and captures successful vehicle maneuvers and learns an optimal policy from those temporal sequences, and when a vehicle approaches the road-side unit, the policy is communicated to the CAV. Deep-imitation learning methodology is proposed to develop such an infrastructure-led policy learning framework

Insights into Simulated Smart Mobility on Roundabouts: Achievements, Lessons Learned, and Steps Ahead

This paper explores the domain of intelligent transportation systems, specifically focusing on roundabouts as potential solutions in the context of smart mobility. Roundabouts offer a safer and more efficient driving environment compared to other intersections, thanks to their curvilinear trajectories promoting speed control and lower vehicular speeds for traffic calming. The synthesis review supported the authors in presenting current knowledge and emerging needs in roundabout design and evaluation. A focused examination of the models and methods used to assess safety and operational performance of roundabout systems was necessary. This is particularly relevant in light of new challenges posed by the automotive market and the influence of vehicle-to-vehicle communication on the conceptualization and design of this road infrastructure. Two case studies of roundabouts were analyzed in Aimsun to simulate the increasing market penetration rates of connected and autonomous vehicles (CAVs) and their traffic impacts. Through microscopic traffic simulation, the research evaluated safety and performance efficiency advancements in roundabouts. The paper concludes by outlining areas for further research and evolving perspectives on the role of roundabouts in the transition toward connected and autonomous vehicles and infrastructures

Applications in Traffic Analysis from Automatically Extracted Road User Interactions with Roadside LiDAR Trajectories

Traffic trajectory data extracted from sensors such as LiDAR and camera has birthed new research areas in the field of transportation. Traffic trajectories is defined at the ability to track road users through time and space at high frequency, typically every tenth of a second. Given this higher granularity of micro traffic data, the behavior of road users can be explored to understand the operational and safety performances on the roads. Therefore, researchers have been stretching the limits to what this type of data can be applied to. Most studies look into the interactions of road users as a surrogate safety measure (SSM) to identify potentially dangerous situation using measures such as post encroachment time (PET) or time to collision (TTC); however, not much applications have been explored outside this. This paper seeks to stretch the imagination for what this type of data can be used for using trajectories generated from roadside LiDAR cloud point data. The first application presented in this paper introduces the first automated method to extract headways and determine capacities at roundabouts entry legs. The method provided accurate capacity results when compared to other standard methods. The second application proposes an automated method to extract pedestrian-vehicle yield rates at uncontrolled crosswalks Pedestrian-vehicle interaction (PVI) analyses from trajectory data has been studied using the SSMs mentioned, but an analysis on yield rates using trajectory data has seldom been performed. This research in traffic trajectory applications paves the way for further applications in traffic safety, operations, and planning

Naturalistic Driver Intention and Path Prediction using Machine Learning

Autonomous vehicles are still yet to be available to the public. This is because there are a number of challenges that have not been overcome to ensure that autonomous vehicles can safely and efficiently drive on public roads. Accurate prediction of other vehicles is vital for safe driving, as interacting with other vehicles is unavoidable on public streets. This thesis explores reasons why this problem of scene understanding is still unsolved, and presents methods for driver intention and path prediction. The thesis focuses on intersections, as this is a very complex scenario in which to predict the actions of human drivers. There is very limited data available for intersection studies from the perspective of an autonomous vehicle. This thesis presents a very large dataset of over 23,000 vehicle trajectories, used to validate the algorithms presented in this thesis. This dataset was collected using a lidar based vehicle detection and tracking system onboard a vehicle. Analytics of this data is presented. To determine the intent of vehicle at an intersection, a method for manoeuvre classification through the use of recurrent neural networks is presented. This allows accurate predictions of which destination a vehicle will take at an unsignalised intersection, based on that vehicle's approach. The final contribution of this thesis presents a method for driver path prediction, based on recurrent neural networks. It produces a multi-modal prediction for the vehicle’s path with uncertainty assigned to each mode. The output modes are not hand labelled, but instead learned from the data. This results in there not being a fixed number of output modes. Whilst the application of this method is vehicle prediction, this method shows significant promise to be used in other areas of robotics

Моделирование безопасного поведения водителя на перекрестках с помощью глубинного обучения

Roundabouts provide safe and fast circulation as well as many environmental advantages, but drivers adopting unsafe behaviours while circulating through them may cause safety issues, provoking accidents. In this paper we propose a way of training an autonomous vehicle in order to behave in a human and safe way when entering a roundabout. By placing a number of cameras in our vehicle and processing their video feeds through a series of algorithms, including Machine Learning, we can build a representation of the state of the surrounding environment. Then, we use another set of Deep Learning algorithms to analyze the data and determine the safest way of circulating through a roundabout given the current state of the environment, including nearby vehicles with their estimated positions, speeds and accelerations. By watching multiple attempts of a human entering a roundabout with both safe and unsafe behaviours, our second set of algorithms can learn to mimic the human’s good attempts and act in the same way as him, which is key to a safe implementation of autonomous vehicles. This work details the series of steps that we took, from building the representation of our environment to acting according to it in order to attain safe entry into single lane roundabouts