Roundabout Scenarios Research Articles

Risk Assessment of Roundabout Scenarios in Virtual Testing Based on an Improved Driving Safety Field.

With the advancement of autonomous driving technology, scenario-based testing has become the mainstream testing method for intelligent vehicles. However, traditional risk indicators often fail in roundabout scenarios and cannot accurately define dangerous situations. To accurately quantify driving risks in roundabout scenarios, an improved driving safety field model is proposed in this paper. First, considering the unique traffic flow characteristics of roundabouts, the dynamic characteristics of vehicles during diverging or merging were taken into account, and the driving safety field model was improved to accurately quantify the driving risks in roundabout scenarios. Second, based on data from the rounD dataset, the model parameters were calibrated using the social force model. Finally, a DENCLUE-like method was used to extract collision systems, calculate vehicle risk degree, and analyze these risks for both the temporal and the spatial dimensions, providing guidance for virtual testing. The proposed method significantly improves detection efficiency, increasing the number of identified dangerous scenarios by 175% compared to the Time to Collision (TTC) method. Moreover, this method can more accurately quantify driving risks in roundabout scenarios and enhance the efficiency of generating dangerous scenarios, contributing to promoting the safety of autonomous vehicles.

Investigation of collision estimation with vehicle and pedestrian using CARLA simulation software

The effectiveness of object detection systems in diverse driving environments is crucial in the growing field of automotive safety. The increasing frequency of traffic accidents, especially at busy intersections with heavy traffic and limited visibility, highlights the pressing requirement for advanced vehicle detection systems. Prior to implementing the real-time experiment, it is advisable first to conduct a simulation in order to gain a deeper understanding of the practical implementation in real-time scenarios. On the other hand, this approach has the potential to reduce both time and cost significantly. The system utilised a software-based solution by implementing the CARLA simulator. This study aims to analyse vehicle detection at T-junctions, cross-junctions, and roundabouts using image data obtained from the CARLA platform. Subsequent analysis differentiates between vehicles and non-vehicle objects in the dataset. The model concludes by proposing Python-based integrative solutions to enhance object detection systems for diverse roads and atmospheric situations. The significance of this study is evaluating the probability of accidents by tracking key factors like vehicle speed, distance, and density on various road types. In future research, it will be essential to investigate how different weather conditions, including rain, haze, and low-light scenarios, affect on sensor performance, specifically LiDAR sensors. Advanced machine learning techniques are proposed to evaluate the effectiveness of the vehicle detection system in collecting key parameters like vehicle count, speed, and distance in junction and roundabout scenarios. These findings have important implications for the advancement of more efficient, context-aware detection systems in the automotive sector.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning, which enables safe autonomous driving on public roads. In this paper, a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method. To begin with, a trajectory prediction model is established based on the graph neural network (GNN) that is trained utilizing the INTERACTION dataset. Then, the validated trajectory prediction model is used to predict the future trajectories of surrounding road users, including pedestrians and vehicles. In addition, a GNN prediction model-enabled motion planner is developed based on the model predictive control technique. Furthermore, two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach, i.e., merging and roundabout scenarios. The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

Speed control with low complexity for multiple autonomous vehicles in roundabouts

The paper introduces a high level speed control method for the coordination of multiple autonomous vehicles (AVs) in roundabout scenarios. The aim of the control method is to guarantee collision-free motion of the AVs, and similarly, to minimize their traveling time. In the method a priority-based ordering process of the AVs is used, which enforces the time-efficient motion of the AVs. The collision-free motion is guaranteed through an optimization-based method including control input constraints. The ordering process and the optimization form a low complexity solution, which requires low computation effort. The proposed control strategy is involved in the high level of a hierarchical control structure. The effectiveness of the proposed control strategy is illustrated by simulation examples and Hardware-in-the-Loop demonstration.

Driving Intention Recognition of Surrounding Vehicles Based on a Time-Sequenced Weights Hidden Markov Model for Autonomous Driving.

Accurate perception, especially situational awareness, is central to the evolution of autonomous driving. This necessitates understanding both the traffic conditions and driving intentions of surrounding vehicles. Given the unobservable nature of driving intentions, the hidden Markov model (HMM) has emerged as a popular tool for intention recognition, owing to its ability to relate observable and hidden variables. However, HMM does not account for the inconsistencies present in time series data, which are crucial for intention recognition. Specifically, HMM overlooks the fact that recent observations offer more reliable insights into a vehicle's driving intention. To address the aforementioned limitations, we introduce a time-sequenced weights hidden Markov model (TSWHMM). This model amplifies the significance of recent observations in recognition by integrating a discount factor during the observation sequence probability computation, making it more aligned with practical requirements. Regarding the model's input, in addition to easily accessible states of a target vehicle, such as lateral speed and heading angle, we also introduced lane hazard factors that reflect collision risks to capture the traffic environment information surrounding the vehicle. Experiments on the HighD dataset show that TSWHMM achieves recognition accuracies of 94.9% and 93.4% for left and right lane changes, surpassing both HMM and recurrent neural networks (RNN). Moreover, TSWHMM recognizes lane-changing intentions earlier than its counterparts. In tests involving more complex roundabout scenarios, TSWHMM achieves an accuracy of 87.3% and can recognize vehicles' intentions to exit the roundabout 2.09 s in advance.

MMH-STA: A Macro-Micro-Hierarchical Spatio-Temporal Attention Method for Multi-Agent Trajectory Prediction in Unsignalized Roundabouts

Simultaneously predicting the future trajectories of heterogeneous multiple agents in a neighborhood is crucial for ensuring the safe and efficient operations of autonomous vehicles in intelligent transportation systems. The unsignalized roundabout is a typical traffic scenario wherein the multivariate and complex information interactions make the prediction task challenging. To address the aforementioned challenges, a macro-micro-hierarchical spatio-temporal attention (MMH-STA) architecture, which can effectively extract the temporal and spatial features of multiple agents based on the interaction mechanism, is presented in this paper. This work makes three contributions: 1) A novel hierarchical framework, which considers the heterogeneity of different types of agents, is proposed for trajectory prediction in the roundabout environment. Similarly, the macro-state for interaction with the roundabout structure and the micro-state for interaction with other agents are introduced for an agent. 2) A heterogeneous graph is devised to represent the spatial interactions of a multi-agent, which is reflected in connections between different types of agents and the properties of nodes and edges in the graph. 3) A novel heterogeneous graph attention network with a multi-order neighborhood is designed to describe the spatial feature interactions in the neighborhood. Finally, a characterized decoder forecasts the future trajectories of multiple agents concomitantly. The experimental results reveal that the proposed model can effectively implement multi-agent trajectory prediction in roundabout scenarios with high accuracy and state-of-the-art performance compared to the baseline.

Design of Unsignalized Roundabouts Driving Policy of Autonomous Vehicles Using Deep Reinforcement Learning

Driving at an unsignalized roundabout is a complex traffic scenario that requires both traffic safety and efficiency. At the unsignalized roundabout, the driving policy does not simply maintain a safe distance for all vehicles. Instead, it pays more attention to vehicles that potentially have conflicts with the ego-vehicle, while guessing the intentions of other obstacle vehicles. In this paper, a driving policy based on the Soft actor-critic (SAC) algorithm combined with interval prediction and self-attention mechanism is proposed to achieve safe driving of ego-vehicle at unsignalized roundabouts. The objective of this work is to simulate a roundabout scenario and train the proposed algorithm in a low-dimensional environment, and then test and validate the policy in the CARLA simulator to ensure safety while reducing costs. By using a self-attention network and interval prediction algorithms to enable ego-vehicle to focus on more temporal and spatial features, the risk of driving into and out of the roundabout is predicted, and safe and effective driving decisions are made. Simulation results show that our proposed driving policy can provide collision risk avoidance and improve vehicle driving safety, resulting in a 15% reduction in collisions. Finally, the trained model is transferred to the complete vehicle system of CARLA to validate the possibility of real-world deployment of the policy model.

Collision-free motion control with learning features for automated vehicles in roundabouts

Control design for safe and time-efficient motion of automated vehicles in roundabout scenarios poses various challenges, especially adaptation to the actual traffic scenario and coordination of the vehicles. This paper proposes the design of a hierarchical motion control with learning feature for roundabout scenarios. The control is designed on two levels, such as on cloud level and on vehicle level. The control on the cloud level is designed by using reinforcement learning (RL), with which the energy efficient motion of the vehicle is achieved. The vehicle level contains a robust controller and a supervisor, with which the collision avoidance of the vehicles is guaranteed. The proposed control on a Hardware-in-the-Loop environment with small-scaled indoor vehicles in augmented reality is implemented. The effectiveness of the control and the safe motion of the automated vehicles under multi-vehicle scenario are demonstrated. The provided scenario illustrates that safe, i.e., collision-free motion of all automated vehicles can be guaranteed.

Comprehensive Safety Evaluation of Highly Automated Vehicles at the Roundabout Scenario

A highly automated vehicle (HAV) is a safety-critical system. Therefore, a verification and validation (V&V) process that rigorously evaluates the safety of HAVs is necessary before their release to the market. In this paper, we propose an interaction-aware safety evaluation framework for the HAV and apply it to the roundabout entering scenario. Instead of assuming that the primary other vehicles (POVs) take predetermined maneuvers, we model the POVs as game-theoretic agents. To capture a wide variety of interactions between the POVs and the vehicle under test (VUT), we use level- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> game theory and social value orientation (SVO) to characterize the interactive behaviors and train a diverse library of POVs using reinforcement learning. The game-theoretic library, together with initial conditions, form a rich testing space for the two-POV roundabout scenario. On the other hand, we propose an adaptive test case generation scheme based on adaptive sampling, stochastic optimization and upper confidence bound (UCB) algorithm to efficiently generate customized challenging cases for the VUT from the testing space. In simulations, the proposed testing space design captured a wide range of interactive patterns at the roundabout scenario. The proposed test case generation scheme was found to cover the failure modes of the VUT more effectively compared to other test case generation approaches.

MPC Tracking Controller Parameters Impacts in Roundabouts

The work is proposed to design a controller of the form known as the roundabout scenario trajectory tracking problem. The road condition is a four-leg, single-lane roundabout; the reference path is given. Due to the decision choice of exits, the MPC tracking controller is used to test the effect of weight parameter and target speed on the performance of the tracking controller. Two sets of test cases are proposed to make the experimental comparison, see the relationship between the control parameters and road conditions (different curvature path), and also see how the weight parameters Q and R and sample time affect the tracking performance. Our work, MPC controller utilization in a roundabout, plays an essential role with the increasing autonomy of vehicles.

Confidence-Aware Reinforcement Learning for Self-Driving Cars

Reinforcement learning (RL) can be used to design smart driving policies in complex situations where traditional methods cannot. However, they are frequently black-box in nature, and the resulting policy may perform poorly, including in scenarios where few training cases are available. In this paper, we propose a method to use RL under two conditions: (i) RL works together with a baseline rule-based driving policy; and (ii) the RL intervenes only when the rule-based method seems to have difficulty handling and when the confidence of the RL policy is high. Our motivation is to use a not-well trained RL policy to reliably improve AV performance. The confidence of the policy is evaluated by Lindeberg-Levy Theorem using the recorded data distribution in the training process. The overall framework is named “confidence-aware reinforcement learning” (CARL). The condition to switch between the RL policy and the baseline policy is analyzed and presented. Driving in a two-lane roundabout scenario is used as the application case study. Simulation results show the proposed method outperforms the pure RL policy and the baseline rule-based policy.

Optimal Trajectory Planning and Robust Tracking Using Vehicle Model Inversion

This article deals with the issue of tracking a reference optimal trajectory for an autonomous nonlinear vehicle model by designing both lateral and longitudinal robust feedback control and a suited feedforward control. In previous works, a strategy based on a human-driver field of view was used to plan an optimal trajectory reference. The optimization has been made using a Genetic Algorithm (GA), and the obtained trajectory has been injected into a Potential Field (PF) so as to be reactive to unforeseen events by using a point mass model. Here, the previously developed GA-PF planification process is integrated in a new complete global planning and tracking method and applied to a validation nonlinear vehicle model. This control tracking method is developed in two strategies: a bicycle model is used as model inversion for feedforward design and a robust control is designed as feedback control in order to take the vehicle (mass and velocity) and road (slope and adherence) parameter variations into account. A lateral nonlinear control and a longitudinal robust control are designed. Realistic autonomous car simulation results are provided on an overtaking scenario and a round-about scenario.

Adaptive Decision-Making for Automated Vehicles Under Roundabout Scenarios Using Optimization Embedded Reinforcement Learning

The roundabout is a typical changeable, interactive scenario in which automated vehicles should make adaptive and safe decisions. In this article, an optimization embedded reinforcement learning (OERL) is proposed to achieve adaptive decision-making under the roundabout. The promotion is the modified actor of the Actor-Critic framework, which embeds the model-based optimization method in reinforcement learning to explore continuous behaviors in action space directly. Therefore, the proposed method can determine the macroscale behavior (change lane or not) and medium-scale behaviors of desired acceleration and action time simultaneously with high sample efficiency. When scenarios change, medium-scale behaviors can be adjusted timely by the embedded direct search method, promoting the adaptability of decision-making. More notably, the modified actor matches human drivers' behaviors, macroscale behavior captures the human mind's jump, and medium-scale behaviors are preferentially adjusted through driving skills. To enable the agent adapts to different types of the roundabout, task representation is designed to restructure the policy network. In experiments, the algorithm efficiency and the learned driving strategy are compared with decision-making containing macroscale behavior and constant medium-scale behaviors of the desired acceleration and action time. To investigate the adaptability, the performance under an untrained type of roundabout and two more dangerous situations are simulated to verify that the proposed method changes the decisions with changeable scenarios accordingly. The results show that the proposed method has high algorithm efficiency and better system performance.

Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines

Efficient trajectory planning of autonomous vehicles in complex traffic scenarios is of interest both academically and in automotive industry. Time efficiency and safety are of key importance and here a two-step procedure is proposed. First, a convex optimization problem is solved, formulated as a support vector machine (SVM), in order to represent the surrounding environment of the ego vehicle and classify the search space as obstacles or obstacle free. This gives a reduced complexity search space and an A* algorithm is used in a state space lattice in 4 dimensions including position, heading angle and velocity for simultaneous path and velocity planning. Further, a heuristic derived from the SVM formulation is used in the A* search and a pruning technique is introduced to significantly improve search efficiency. Solutions from the proposed planner is compared to optimal solutions computed using optimal control techniques. Three traffic scenarios, a roundabout scenario and two complex takeover maneuvers, with multiple moving obstacles, are used to illustrate the general applicability of the proposed method.

Occlusion-Aware Motion Planning at Roundabouts

In this work, we present a motion planning framework for automated vehicles to drive safely through intersections despite occlusions and the uncertain behavior of the surrounding vehicles. A context representation based on probably-free gaps is proposed as a means to provide, in occluded scenes, richer information to the motion planner compared to representations only based on observed objects. Our solution builds upon the path-velocity decomposition approach. Path planning is performed with state-of-the-art techniques, while a novel trajectory abstraction is used to reason about speed profiles without explicitly generating sequences of accelerations. To efficiently identify the best safe speed profile for every path candidate, a reachability-based analysis is also formulated. The proposed planning workflow is evaluated in roundabout scenarios. Our simulation study shows that the proposed context representation facilitates the decision-making in occluded scenes and that the reachability-based planning strategy is robust, computationally efficient, and outperforms a simpler reactive strategy.

A Comparison of Traffic Flow Performance of Roundabouts and Signalized Intersections: A Case Study in Nigde

Background: Intersections affect the safety and capacity of urban traffic. Therefore, the design and selection of the type of intersection need to be made very carefully. According to the demand level, a different intersection can be designed. Signalized intersections are one of the intersection types in which the sequence and duration of the flow at the intersection are provided by the lights. Generally, this type of intersection is used on roads with high traffic volume. Modern roundabouts are one of the types of circular intersections that provide advantages over other types of intersection in terms of smooth operation and safety. Modern roundabouts exist in several types today worldwide. In practice, the distinction about the kinds of roundabouts would not be fully clarified; as a result, queuing and delay can be seen as negative effects. Methods: In this study, to make a distinction and clarify the kinds of roundabouts, first, the roundabouts types are introduced according to geometric and operational aspects. A signalized intersection, where a circular island is placed and also signalized, was investigated in terms of capacity, delay, and emissions located in Niğde. The traffic flow performance of the current state (nested signalized roundabout) was calculated with HCM Method (for signalized intersection) using SIDRA and compared with roundabout solutions of the intersection with HCM6 (for roundabout) method using SIDRA Intersection analysis software. Results: From the results of the intersection capacity analysis study based on HCM6, it was seen that the application of a roundabout scenario (intersection considered as a modern roundabout) showed higher performance at the intersections than the intersection having a secondary signal. Capacity increased to 67.8%, the average delay decreased to 72.8% and 95th percentile queue dropped to 82.2%. Conclusion: Roundabout controlling instead of a nested signal system can be an example of the increase in the performance of traffic flow. This highlights the importance of choosing the appropriate roundabout design.

Adaptive sampling-based motion planning with a non-conservatively defensive strategy for autonomous driving

Abstract Sampling-based motion planning methods are widely adopted in autonomous driving. Typically, sampling can be decoupled into two layers: a path sampling layer and a speed profile sampling layer. For the path sampling layer, traditional methods tend to sample with a uniform distribution over the whole feasible space, which might cause either computational inefficiency or poor performance if the sampling resolution is not set appropriately. To solve this problem, we propose an adaptive path sampling approach that samples from a time-varying distribution depending on the dynamic environment and potential costs of the ego vehicle. Such sampling strategy is then combined with a non-conservatively defensive strategy in the speed sampling layer to generate a set of safe but not overcautious trajectories. The proposed motion planning framework is tested both in simulation and a real autonomous vehicle in a roundabout scenario. The results demonstrate that it can efficiently generate non-conservative but defensive trajectories to safely drive the vehicles in dynamic environments full of uncertainties.

Safety Improvements by Converting a Standard Roundabout with Unbalanced Flow Distribution into an Egg Turbo Roundabout: Simulation Approach to a Case Study

In 2011, the United Nations Road Safety Collaboration (UNRSC) developed a Global Plan for the Decade of Action for Road Safety 2011–2020. Among the categories or “pillars” of activities, is the improvement of road safety for infrastructures. Furthermore, this plan is aligned by the UN Sustainable Development Goals that included even traffic safety. In this regard, this study estimates safety improvements achieved by converting a standard roundabout into an egg turbo roundabout. In particular, turbo roundabouts have become very popular in Northern Europe for both their safety and their capacity. Many studies have shown these advantages thanks to their features: preventive separation of entering flows, limited lane changing and low speeds due to curbs. Given the absence of existing turbo roundabouts in Italy, this research studied and compared a “virtual” roundabout with spiraling circular carriageways to an existing multi-lane roundabout in order to assess its significant reduction in terms of potential collisions. This study relied on traffic conflicts in micro-simulation by using VISSIM software and then Surrogate Safety Assessment Model (SSAM). The research is based on the traffic process observed at a standard roundabout in Cosenza (Italy) marked by a high level of congestion and safety problems. Speeds, critical gaps, queue lengths, and floating car data, obtained from video observations, have been used as input data for the calibration procedure of the first scenario (case study roundabout). Then, the turbo roundabout solution was built and simulated by using the previously derived parameters. Finally, the two roundabout scenarios were compared in terms of spatial distribution of the potential conflicts determined by SSAM. The results could help to measure the performance and safety impact of these two roundabout configurations.

Restraint device for airway management in low-birthweight infants

Low-birthweight infants are at an increased risk of a compromised airway when restrained in a child safety seat (CSS). Neonatal intensive care units (NICUs) use rolled towels and other positioning aids with a variety of unique techniques and methodologies to adapt a CSS to the specialised needs of this particular population at the time of discharge. A preemie positioning device (PPD) was developed as an enhancement to the existing occupant restraints, taking into consideration infant anthropometry, materials with suitable properties and analyses of infant airway patency. A numerical model consisting of a five-point CSS with the PPD and a novel anthropomorphic testing device representing a small infant weighing less than 2.5 kg was developed. The performance of the PPD was assessed through observation of the angle of the neck in flexion–extension bending during driving scenarios which may inhibit the respiratory function of a low-birthweight infant. Three foam materials and three PPD geometries exhibited more favourable neck angles with the PPD. In braking and roundabout scenarios the neck angle was increased in the range of 10°–30°. A sharp-turn manoeuvre yielded similar results with and without the PPD in terms of the final position of the head with respect to the torso. The proposed positioning aid is predicted to reduce the likelihood of respiratory compromise resulting from repositioning of the infant due to vehicle excitations. Analyses of crash performance indicate that with proper selection of the foam the PPD has similar or improved performance over a commercial CSS.

Sensitivity analysis in traffic microscopic simulation model for roundabouts

In the literature, many analytical techniques allow the study of the performance (capacity, queues, delays, etc) of roundabouts which are divided into two groups: analytical models and microscopic simulation models. Each method, when formulated, has to consider some aspects of roundabout circulation in comparison to others (geometric elements, vehicular flow and user behaviour). In particular, microscopic simulation models offer several advantages, compared to analytical ones (such as realistic modelling of vehicle arrivals and departures or the ability to study the spatial extent of queues), but they must be carefully calibrated. In this paper the authors introduce the results of a large survey conducted on an ample range of roundabout scenarios through the application of VISSIM simulation software. Each scenario describes a fixed roundabout phenomenon using geometric elements (inscribed circle radius, circular roadway, central and splitter islands etc.) and characteristics of the traffic flow (density, distribution, crossing and approach speeds, etc.) as variables. The results allow authors to evaluate the effects of these parameters on roundabout stop-line delays through a significance analysis.