Vehicle Interaction Research Articles

Merging planning in dense traffic scenarios using interactive safe reinforcement learning

Autonomous navigation in dense traffic scenarios, such as on-ramp forced merging, still poses significant challenges for autonomous vehicles to prevent accidents and alleviate traffic congestion. This paper introduces a novel motion planning framework that combines Interactive Safe Reinforcement Learning (IntSRL) with Nonlinear Model Predictive Control (NMPC). This framework develops an interactive merging planning policy that accounts for the uncertainty of traffic participants, multi-objective optimization and heterogeneous vehicle interactions, in which the upper planner, i.e., IntSRL, furnishes the lower planner, NMPC, with global guidance path and velocity guidance. An Adaptive Safety Governor (ASG) module within IntSRL adjusts potentially unsafe actions by incorporating prior knowledge and driving experience. And a coupling evaluation mechanism for multi-objective optimization is embedded into reward shaping with integration of driving safety and strategy efficiency. We evaluate the proposed controller on various dense traffic scenarios using the proposed Heterogeneous Intelligent Driver Model (H-IDM) considering different driving styles and cooperative willingness of other vehicles. The test results indicate that the proposed method surpasses existing optimization-based and learning-based baselines in qualitative and quantitative measures.

Metamodeling on uncertainty quantification in the behavior of the tire/road interaction of vehicles

In recent years, the automotive industry has been developing applied research to meet customer’s needs; considering safety, vehicle comfort and energetic efficiency. In particular, automotive tires have a prominent position in this research area, ensuring good vehicle handling, comfort and safety. In the vehicle dynamics performance, excellent gripping and reduced rolling resistance in the tires are crucial to maximize the energetic efficiency in a reliable way. However, to ensure such a level of reliability in vehicle operation, the inherent uncertainties of tires, as well as other factors subject to variability must be taken into account in the vehicle design. In this way, the present paper analyzes in detail the effect of variations in some parameters such as ambient temperature, ground conditions, vertical load, speed and tire inflation on the analysis of vehicle dynamics. A Metamodeling approach associated with the Monte Carlo Simulation was employed to develop the mathematical models to analyze the effect of uncertain parameters on the tire rolling resistance; traction, centripetal and lateral forces, using experimental data from the literature, in the longitudinal and lateral vehicle dynamics. Therefore, the present research brings as an innovation an integrated approach to the input parameters of the system with the rolling resistance through the developed metamodels. There was a substantial variability of up to 15% both up and down in the Maximum Traction Force of a vehicle in response to variations in the vehicle’s weight and the coefficient of tire rolling resistance. In contrast, the Lateral Force exhibited a greater variability, with a 25 downward and 10% upward variation associated with the weight and friction coefficient variability of the vehicle. Further investigations into the sensitivity analysis highlight the significant influence of the friction coefficient and temperature on the Traction Forces of the vehicle.

A Multi-Agent Driving-Simulation Approach for Characterizing Hazardous Vehicle Interactions between Autonomous Vehicles and Manual Vehicles

The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question ‘What road condition would make the mixed car-following situations hazardous?’ Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety.

Lane-change intention recognition considering oncoming traffic: Novel insights revealed by advances in deep learning

Lane-changing (LC) intention recognition models have seen limited real-world application due to a lack of research on two-lane two-way road environments. This study constructs a high-fidelity simulated two-lane two-way road to develop a Transformer model that accurately recognizes LC intention. We propose a novel LC labelling algorithm combining vehicle dynamics and eye-tracking (VEL) and compare it against traditional time window labelling (TWL). We find the LC recognition accuracy can be further improved when oncoming vehicle features are included in the LC dataset. The Transformer demonstrates state-of-the-art performance recognizing LC 4.59 s in advance with 92.6 % accuracy using the VEL labelling method compared to GRU, LSTM and CNN + LSTM models. To interpret the Transformer's 'black box', we apply LIME model which reveals the model focuses on eye-tracking features and LC vehicle interactions with preceding and oncoming traffic during LC events. This research demonstrates that modelling additional road users and driver gaze in LC intention recognition achieves significant improvements in model performance and time-to-collision warning capabilities on two-lane two-way roads.

Multi-modality trajectory prediction with the dynamic spatial interaction among vehicles under connected vehicle environment

Compared to non-connected vehicle environments, the connected vehicle environment establishes vehicle interconnection through communication technologies, resulting in more complex interaction, network topologies, and large-scale inputs. This complexity renders traditional trajectory prediction models, which rely primarily on inputting historical information of the target vehicle, inadequate for handling the complex and dynamic interactive lane-changing scenarios in connected vehicle environments. In a connected vehicle environment, it is necessary to propose a more targeted and stable lane-changing behavior prediction method based on vehicle traveling characteristics. Taking into account dynamic spatial interaction among vehicles, this study proposes a multi-modality trajectory prediction model called STA-LSTM to perform analysis on the potential interactive behaviors among vehicles under connected vehicle lane-changing scenarios, and specifically to expand the multi-modality feature input of the vehicle trajectory prediction model. The spatial grid occupancy method is used to model the interactions between vehicles. A space-dimensional attention mechanism is introduced to adaptively match the influencing weights of the surrounding vehicles with the target vehicle and to improve the interactive information extraction method. In addition, the attention module is incorporated into the LSTM decoder from the time dimension so that the established model can identify significant historical hidden features during each trajectory decoding process. To account for the uncertainty of trajectory prediction, the vectors of vehicle interactions are incorporated into contextual information to improve the reliability of prediction results and the robustness of the established model. Compared with conventional baseline models, the proposed model exhibited lower root mean square error (RMSE) and negative log-likelihood (NLL) values, and the RMSE values at different prediction times of 1s, 2s, 3s, 4s, and 5s are 0.46m, 1.15m, 1.89m, 2.84m, and 4.05m, respectively. This indicates that the proposed model can accurately predict the interactions between vehicles and the travel paths of surrounding target vehicles.

AI in Autonomous Vehicles

The integration of Artificial Intelligence (AI) in autonomous vehicles represents a transformative leap towards safer, more efficient, and technologically advanced transportation systems. This abstract provides a comprehensive overview of the dynamic landscape where AI converges with autonomous vehicles, examining the synergies, challenges, and far-reaching implications of this groundbreaking integration AI-Powered Perception and Decision-Making: Delving into the technological core, the abstract explores how AI empowers autonomous vehicles with sophisticated perception systems, such as computer vision and sensor fusion. It discusses the role of machine learning algorithms in real-time decision-making, enabling vehicles to adapt to dynamic road conditions and unforeseen circumstances. Challenges in Autonomy: Recognizing the complexity of autonomous systems, the abstract addresses challenges such as handling edge cases, ensuring robustness against adversarial attacks, and navigating regulatory and ethical considerations. It emphasizes the importance of addressing these challenges to foster public trust and acceptance of autonomous vehicles. Human-AI Interaction in Autonomous Vehicles: Examining the interface between humans and AI-driven vehicles, the abstract discusses the importance of designing intuitive and trustworthy communication channels. It explores advancements in natural language processing and gesture recognition, fostering seamless collaboration between humans and autonomous systems. Regulatory Landscape and Ethical Considerations: Recognizing the pivotal role of regulations, the abstract discusses the evolving regulatory landscape for autonomous vehicles. It delves into ethical considerations surrounding AI decisions in critical situations, underscoring the need for a harmonized approach to ensure responsible AI deployment in autonomous driving scenarios.

Framework to Identify Vehicle Platoons under Heterogeneous Traffic Conditions on Urban Roads

Vehicle platoon studies are essential for understanding and managing traffic on urban arterial roads. The identification of vehicle platoons on urban roads has drawn more attention in recent years. Researchers have been exploring various methods and algorithms to detect and classify platoons, as well as investigating the benefits and implications of their presence on road capacity, safety, fuel consumption, and environmental pollution. The present study formulated a three-step strategy to identify vehicle platoons in the urban road network under heterogeneous traffic conditions. The proposed three steps are recognizing vehicle interaction, the estimation of critical headway, and vehicle platoon identification. Traffic data were collected for 13 h in a six-lane divided urban arterial road using an infrared sensor. A Python program was developed to recognize vehicle platoons. The results revealed that out of a total of 42,500 vehicles observed, 74% of vehicles were in vehicle platoons. The characteristics of the identified vehicle platoons were studied, thus focusing on key aspects such as platoon size, intra-platoon headway, platoon stream speed, and vehicle composition in the platoon. The results revealed a linear relationship between the percentage of vehicles in the platoon and traffic volume. The findings of the study will be beneficial in examining platoon-based data aggregation, the utilization of road capacity, and traffic flow optimization.

The role of individual differences in human-automated vehicle interaction

As more automation is integrated into vehicles, understanding how humans interact with these new technologies is becoming increasingly important given the high cost of errors. Cognitive models have the potential to provide insights into human-automated vehicle interaction and inform risk assessment, user interface design, and risk mitigation interventions. We argue that accounting for individual differences is necessary in order to derive the full benefits of cognitive models. We describe several methods for modeling individual differences and demonstrate potential pitfalls of using a one-size-fits-all model. In addition, we explain how modeling individual differences is important for risk assessment, designing robust user interfaces and automated systems, and designing effective risk mitigation interventions. Finally, we use a simulation study to demonstrate possible benefits of modeling individual differences in unmanned vehicle management.

Traffic Performance of Successive Freeway Merge Segments

At freeway interchanges with high entering flows, implementing two successive merges can improve traffic flow and capacity by distributing the merging maneuvers. The German Guidelines for the Design of Motorways define different types of successive (“double”) merges. According to the quality of service assessment procedure for freeway merge segments given in the German Highway Capacity Manual, successive merge segments can be evaluated separately. However, depending on the distance between the merges, the capacity of the second (downstream) merge might be affected by the entering traffic at the first (upstream) merge, which influences the lane-flow distribution. For a more precise evaluation of traffic flow performance and traffic safety depending on the geometric design characteristics, vehicle interactions, lane changes, the resulting capacity of successive merge segments, accident rates, and crash types were analyzed in the study. The research was based on loop detector data as well as trajectory data obtained from video measurements, which provide an insight into the interactions between mainline and entering vehicles. For the analysis of traffic safety, crash data were evaluated over a period of 3 years. Furthermore, successive merge segments with different geometric and traffic characteristics were modeled with the microscopic traffic simulation tool BABSIM to analyze the influence on traffic flow and capacity. As a result, capacity models and recommendations for the geometric design of successive freeway merge segments are provided.

A large-scale hybrid micro- and mesoscopic simulation approach for railway operation

In this study, the existing agent-based simulation framework MATSim is extended towards a module for the simulation of trains and their interactions with the infrastructure. The proposed railway simulation module railsim offers several advantages over existing railway planning tools: railsim is open-source, easily adaptable, allows for a microscopic, mesoscopic or hybrid micro-mesoscopic simulation setup and is fully integrated in an existing simulation framework. Trains have a spatial dimension along several infrastructure segments. Trains accelerate and decelerate based on vehicle- and infrastructure-specific attributes as well as the available capacity ahead of the vehicle. The interaction of vehicles as well as the modeling of the infrastructure is adapted to the railway system. Trains are enabled to reserve infrastructure elements (fixed block segments, moving blocks), and reservation requests interact with each other. The illustrative Switzerland case study reveals that an overall unique capacity of 3 trains per section and 10 trains per station ensures most trains reach their destinations successfully, while lower capacities lead to insufficient infrastructure and train disruptions.

Scenario-Tree Model Predictive Control for Vehicle Interactions in Highway Setting

Scenario-Tree Model Predictive Control for Vehicle Interactions in Highway Setting

Simulation of Vehicle Interaction Behavior in Merging Scenarios: A Deep Maximum Entropy-Inverse Reinforcement Learning Method Combined With Game Theory

Simulation of Vehicle Interaction Behavior in Merging Scenarios: A Deep Maximum Entropy-Inverse Reinforcement Learning Method Combined With Game Theory

IMA‐LSTM: An Interaction‐Based Model Combining Multihead Attention with LSTM for Trajectory Prediction in Multivehicle Interaction Scenario

The rapid development of vehicle‐to‐vehicle (V2V) communication technology provides more opportunities to improve traffic safety and efficiency, which facilitates the exchange of multivehicle information to mine potential patterns and hidden associations in vehicle trajectory prediction. To address the importance of fine‐grained vehicle interaction modelling in vehicle trajectory prediction, the present work proposes an integrated vehicle trajectory prediction model that combines the multihead attention mechanism with long short‐term memory (IMA‐LSTM) in multivehicle interaction scenarios. Compared to existing studies, a dedicated feature extraction module including both individual features and interactive features is designed and sophisticated multihead attention mechanism is applied with LSTM framework to capture the variation of spatial‐temporal interactions between vehicles. The performance of the proposed model in different scenarios is examined using both the high‐D and the NGSIM dataset through comprehensive comparison experiments. The results indicate that the proposed IMA‐LSTM model presents great improvement in vehicle trajectory prediction performance in different scenarios compared to the model that does not consider multivehicle interaction features. Moreover, it outperforms other existing models in 3–5s prediction horizons and such superiority is more evident in left lane‐changing (LLC) scenarios than lane‐keeping (LK) and right lane‐changing (RLC) scenarios. The outcomes fully address the importance of fine‐grained interactive feature modelling in improving vehicle trajectory performance in complex multivehicle interaction scenarios and could further contribute to more refined traffic safety and traffic efficiency management.

YOLOV4 Deepsort ANN for Traffic Collision Detection

Every collision must be handled right away to prevent further harm, damage, and traffic bottlenecks. Hence, the implementation of a systematic approach for accident detection becomes imperative to expedite response mechanisms. Our proposed accident detection system operates in three stages, encompassing vehicle object detection, multiple object tracking, and vehicle interaction analysis. YOLOv4 is employed for object detection, while DeepSort is utilized to the tracking of multiple vehicle objects. Subsequently, the positional and interactional data of each object within the video frame undergo thorough analysis to identify collisions, utilizing an Artificial Neural Network (ANN). Notably, collisions involving a single vehicle and not affecting other road users are excluded from the scope of this study. The evaluation of our approach reveals that the ANN model achieves a commendable F-Measure of 0.97 for detecting objects without collisions and 0.88 for objects involved in collisions, based on the conducted tests.

Spatio-Temporal-Attention-Based Vehicle Trajectory Prediction Considering Multi-Vehicle Interaction in Mixed Traffic Flow

As a link connecting the environmental perception system and the decision-making system, accurate obstacle trajectory prediction provides a reliable guarantee of correct decision-making by autonomous vehicles. Oriented toward a mixed human-driven and machine-driven traffic environment, a vehicle trajectory prediction algorithm based on an encoding–decoding framework composed of a multiple-attention mechanism is proposed. Firstly, a directed graph is used to describe vehicle–vehicle motion dependencies. Then, by calculating the repulsive force between vehicles using a priori edge information based on the artificial potential field theory, vehicle–vehicle interaction coefficients are extracted via a graph attention mechanism (GAT). Subsequently, after concatenating the vehicle–vehicle interaction feature with the encoded vehicle trajectory vectors, a spatio-temporal attention mechanism is applied to determine the coupling relationship of hidden vectors. Finally, the predicted trajectory is generated by a gated recurrent unit (GRU) decoder. The training and evaluation of the proposed model were conducted on the NGSIM public dataset. The test results demonstrated that compared with existing baseline models, our approach has fewer prediction errors and better robustness. In addition, introducing artificial potential fields into the attention mechanism causes the model to have better interpretability.

Developing rear-end and side-swipe conflict prediction models for urban signalized intersections under disordered traffic conditions

A Traffic Conflict Technique is a proactive approach that identifies observable critical vehicle interactions (conflict) that could have led to a crash. This paper develops conflict-based safety performance functions (SPFs) to predict the number of traffic conflicts at the signal cycle level in mixed traffic conditions with poor lane discipline (disordered traffic conditions). 9586 vehicle trajectories were extracted from traffic video data collected from 4 signalized intersections in India. Critically interacting vehicle pairs were estimated using Time to Collision (TTC). The conventional surrogate measure is modified into a 2-dimensional surrogate approach which captures the conflicts by incorporating the vehicle dimension, heading direction, position, speed, and acceleration in the longitudinal and lateral direction between vehicles. Rear-end and side-swipe conflicts are identified at varying threshold values to incorporate severity. Rear-end conflict SPFs show that higher conflict occurrence is expected during signal cycles with more traffic volume, higher vehicle arrival speed, more right-turning traffic, and a lower platoon ratio. At severe threshold (TTC ≤ 1 s), one 1 m/s increase in speed of vehicle increases the expected number of rear-end conflicts by 6%. Furthermore, SPFs show that severe side-swipe conflicts are expected during signal cycles with more lane-changing maneuvers and right-turning vehicles. For TTC ≤ 1 s, one unit increase in the number of lane changes would increase the expected number of sides-wipe conflicts by 7.9%. The finding of this study can be most beneficially used for arriving at policy measures and improving the safety of signalized intersections in disordered traffic conditions.

Review of Intelligent Vehicle Driving Risk Assessment in Multi-Vehicle Interaction Scenarios

With the rapid breakthroughs in artificial intelligence technology and intelligent manufacturing technology, automotive intelligence has become a research hotspot, and much progress has been made. However, a skeptical attitude is still held towards intelligent vehicles, especially when driving in a complex multi-vehicle interaction environment. The interaction among multi-vehicles generally involves more uncertainties in vehicle motion and entails higher driving risk, and thus deserves more research concerns and efforts. Targeting the safety assessment issue of complex multi-vehicle interaction scenarios, this article summarizes the existing literature on the relevant data collection methodologies, vehicle interaction mechanisms, and driving risk evaluation methods for intelligent vehicles. The limitations of the existing assessment methods and the prospects for their future development are analyzed. The results of this article can provide a reference for intelligent vehicles in terms of timely and accurate driving risk assessment in real-world multi-vehicle scenarios and help improve the safe driving technologies of intelligent vehicles.

Emerging opportunities for wildlife conservation with sustainable autonomous transportation

Autonomous vehicles (AVs) are expected to play a key role in the future of transportation, and to introduce a disruptive yet potentially beneficial change for wildlife–vehicle interactions. However, this assumption has not been critically examined, and reducing the number of wildlife–vehicle collisions (WVCs) may be beyond current technological capabilities. Here, we introduce a conceptual framework encompassing the intersection between AV technology and wildlife conservation to reduce WVCs. Our proposed framework integrates the development of robust warning systems and animal detection methods for AV systems, and the incorporation of wildlife–vehicle interactions into decision‐making algorithms. With large‐scale AV deployment a looming reality, it is vital to incorporate conservation and sustainability into the societal, ethical, and legal implications of AV technology. We intend our framework to help ecologists and conservationists foster the necessary interdisciplinary collaborations with AV developers and policy makers to reduce WVCs and concomitant biodiversity loss.

The Interaction of Electric Vehicles with the Power Grid-Challenges and Solutions

The Interaction of Electric Vehicles with the Power Grid-Challenges and Solutions

Pedestrians’ road-crossing decisions: Comparing different drift-diffusion models

The decision of whether to cross a road or wait for a car to pass, humans make frequently and effortlessly. Recently, the application of drift-diffusion models (DDMs) on pedestrians’ decision-making has proven useful in modelling crossing behaviour in pedestrian–vehicle interactions. These models consider binary decision-making as an incremental accumulation of noisy evidence over time until one of two choice thresholds (to cross or not) is reached. One open question is whether the assumption of a kinematics-dependent drift-diffusion process, which was made in previous pedestrian crossing DDMs, is justified, with DDM-parameters varying over time according to the developing traffic situation. It is currently unknown whether kinematics-dependent DDMs provide a better model fit than conventional DDMs, which are fitted per condition. Furthermore, previous DDMs have not considered reaction times for the not-crossing option. We address these issues by a novel experimental design combined with modelling. Experimentally, we use a 2-alternative-forced-choice paradigm, where participants view videos of approaching cars from a pedestrian’s perspective and respond whether they want to cross before the car or to wait until the car has passed. Using these data, we perform thorough model comparison between kinematics-dependent and condition-wise fitted DDMs. Our results demonstrate that condition-wise fitted DDMs can show better model fits than kinematics-dependent DDMs as reflected in the mean-squared-errors. The condition-wise fitted models need considerably more parameters, but in some cases still outperform kinematics-dependent DDMs in measures that penalize the parameter number (e.g., Akaike information criterion). Introducing a starting point bias provides support for the novel hypothesis of rapid early evidence build-up from the initial view of the vehicle distance. The drift rates obtained for the condition-wise fitted models align with the assumptions in the kinematics-dependent models, confirming that pedestrians’ decision processes are kinematics-dependent. However, the partial preference for condition-wise fitted models in the model selection suggests that the correct form of kinematics-dependence has not yet been identified for all DDM-parameters, indicating room for improvement of current pedestrian crossing DDMs. Developing more accurate models of human cognitive processes will likely facilitate autonomous vehicles to understand pedestrians’ intentions as well as to show unambiguous human-like behaviour in future traffic interactions with humans.