Intelligent Driver Research Articles

Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic

Road traffic crashes are a major safety problem, with one of the leading factors in crashes being human error. Automated and connected vehicles (CAVs) that are equipped with Advanced Driver Assistance Systems (ADAS) are expected to reduce human error. In this paper, the Simulation of Urban MObility (SUMO) traffic simulator is used to investigate how CAVs impact road safety. In order to define the longitudinal behavior of Human Drive Vehicles (HDVs) and CAVs, car-following models, including the Krauss, the Intelligent Driver Model (IDM), and Cooperative Adaptive Cruise Control (CACC) car-following models were used to simulate CAVs. Surrogate safety measures were utilized to analyze CAVs’ safety impact using time-to-collision. Two case studies were evaluated: a signalized grid network that included nine intersections, and a second network consisting of an unsignalized intersection. The results demonstrate that CAVs could potentially reduce the number of conflicts based on each of the car following model simulations and the two case studies. A secondary finding of the research identified additional safety benefits of vehicles equipped with collision avoidance control, through the reduction in rear-end conflicts observed for the CACC car-following model.

Reinforcement Learning-Based Control of Signalized Intersections Having Platoons

Smart transportation cities are based on intelligent systems and data sharing, whereas human drivers generally have limited capabilities and imperfect traffic observations. The perception of Connected and Autonomous Vehicle (CAV) utilizes data sharing through Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communications to improve driving behaviors and reduce traffic delays and fuel consumption. This paper proposes a Double Agent (DA) intelligent traffic signal module based on the Reinforcement Learning (RL) method, where the first agent, the Velocity Agent (VA) aims to minimize the fuel consumption by controlling the speed of platoons and single CAVs crossing a signalized intersection, while the second agent, the Signal Agent (SA) proceeds to efficiently reduce traffic delays through signal sequencing and phasing. Several simulation studies have been conducted for a signalized intersection with different traffic flows and the performance of the single-agent with only VA, DA with both VA and SA, and Intelligent Driver Model (IDM) are compared. It is shown that the proposed DA solution improves the average delay by 47.3% and the fuel efficiency by 13.6% compared to the Intelligent Driver Model (IDM).

Cooperative Control in Eco-Driving of Electric Connected and Autonomous Vehicles in an Un-Signalized Urban Intersection

This paper addresses the problem of finding the optimal Eco-Driving (ED) speed profile of an electric Connected and Automated Vehicle (CAV) in an isolated urban un-signalized intersection. The problem is formulated as a single-level optimization and solved using Pontryagin's Minimum Principle (PMP). Analytical solutions are presented for various conflicts that occur at an intersection. Cooperation is introduced amongst CAVs as the ability to share intentions. Two levels of cooperation, namely the Cooperative ED (C-ED) and Non-Cooperative (NC-ED) algorithms are evaluated, in a simulation environment, for energy efficiency with Intelligent Driver Model (IDM) as the baseline.

Cooperative Eco-driving Controller for Battery Electric Vehicle Platooning

Eco-driving and platooning both play an increasingly important role in alleviating range anxiety for battery electric vehicles (BEVs). Pontryagin's Minimum Principle (PMP) has been popularly adopted for designing eco-driving speed planners because of its capability to achieve near optimal solutions at high computational efficiency that brings potential of real-time implementation. However, most of the existing PMP-based eco-driving studies did not consider the change of aerodynamic drag coefficient due to platooning effect, or simply ignore the nonlinear aerodynamic resistance to achieve analytical optimal solutions. Such simplification of aerodynamic resistance model can lead to inaccuracy in vehicle speed optimization, and thus potentially make eco-driving less energy-efficient and cause severe safety issues in car following scenario. In recognition of these issues, this paper proposes an innovative PMP-based cooperative eco-driving controller (CEDC) that incorporates the impact of air drag coefficient change in the optimal speed trajectory design for the circumstance of two-BEV operation. The simulation results reveal noticeable differences in optimal speed profile, when compared to baseline CEDC controller which adopts a constant aerodynamic drag coefficient. It is also shown that implementation of the optimal speed profile from baseline CEDC controller will violate safety constraint and lead to collision. Furthermore, the simulation results demonstrate that, the CEDC can reduce energy consumption by 12.25% and 4.68%, respectively, when compared to intelligent driver model (IDM)-based control and the baseline CEDC.

Mixed traffic flow of human-driven vehicles and connected autonomous vehicles: String stability and fundamental diagram.

The introduction of connected autonomous vehicles (CAVs) gives rise to mixed traffic flow on the roadway, and the coexistence of human-driven vehicles (HVs) and CAVs may last for several decades. CAVs are expected to improve the efficiency of mixed traffic flow. In this paper, the car-following behavior of HVs is modeled by the intelligent driver model (IDM) based on actual trajectory data. The cooperative adaptive cruise control (CACC) model from the PATH laboratory is adopted for the car-following model of CAVs. The string stability of mixed traffic flow is analyzed for different market penetration rates of CAVs, showing that CAVs can effectively prevent stop-and-go waves from forming and propagating. In addition, the fundamental diagram is obtained from the equilibrium state, and the flow-density chart indicates that CAVs can improve the capacity of mixed traffic flow. Furthermore, the periodic boundary condition is designed for numerical simulation according to the infinite length platoon assumption in the analytical approach. The simulation results are consistent with the analytical solutions, suggesting the validity of the string stability and fundamental diagram analysis of mixed traffic flow.

An Intelligent Driving Assistance System Based on Lightweight Deep Learning Models

An intelligent driver assistance system is developed in this study, which is able to remind the drivers to turn on the head lights or wipers through situation recognition method when driving at night or on rainy days. Furthermore, it is also able to alarm the drivers based on the lightweight deep learning model and the distance estimation method when surrounding vehicles are too close. Experimental results show that the proposed methods and the chosen lightweight model in our proposed system obtain reliable performance and sufficient computational efficiency under limited computing resource. In conclude, our proposed system obtains high probability to be adopted for the development of advanced driver assistance systems (ADAS).

Estimation of Varying Reaction Times with RNN and Application to Human-like Autonomous Car-following Modeling

The interaction between human-driven vehicles and autonomous vehicles has become a vital issue in micro-transportation science. Compared to autonomous vehicles, human-driven vehicles have varying reaction times that could compromise traffic efficiency and stability. But human drivers can anticipate future traffic conditions subconsciously, which guar-antees qualified performance. This paper proposes an estimation method of varying reaction times and a human-like autonomous car-following model. The varying reaction times are estimated based on recurrent neural networks (RNNs) after the cross-correlation analysis of human-driven vehicles’ trajectory profiles. A human-like autonomous car-following model is established based on Intelligent Driver Model (IDM), considering both varying reaction times and temporal anticipation, and the short form is IDM RTTA. The analytical string stability of IDM RTTA is deduced and illustrated. The trajectory simulation result shows that increasing accuracy of trajectory prediction is obtained with the proposed model, which will benefit the interaction between human-driven vehicles and autonomous vehicles.

A comparative study of energy management systems under connected driving: Cooperative car-following case

In this work, we propose connected energy management systems for a cooperative hybrid electric vehicle (HEV) platoon. To this end, cooperative driving scenarios are established under different car-following behavior models using connected and automated vehicles technology, leading to a cooperative cruise control system (CACC) that explores the energy-saving potentials of HEVs. As a real-time energy management control, an equivalent consumption minimization strategy (ECMS) is utilized, wherein global energy-saving is achieved to promote environment-friendly mobility. The HEVs cooperatively communicate and exchange state information and control decisions with each other by sixth-generation vehicle-to-everything (6G-V2X) communications. In this study, three different car-following behavior models are used: intelligent driver model (IDM), Gazis–Herman–Rothery (GHR) model, and optimal velocity model (OVM). Adopting cooperative driving of six Toyota Prius HEV platoon scenarios, simulations under New European Driving Cycle (NEDC), Worldwide Harmonized Light Vehicle Test Procedure (WLTP), and Highway Fuel Economy Test (HWFET), as well as human-in-the-loop (HIL) experiments, are carried out via MATLAB/Simulink/dSPACE for cooperative HEV platooning control via different car-following-linked-vehicle scenarios. The CACC-ECMS scheme is assessed for HEV energy management via 6G-V2X broadcasting, and it is found that the proposed strategy exhibits improvements in vehicular driving performance. The IDM-based CACC-ECMS is an energy-efficient strategy for the platoon that saves: (i) 8.29% fuel compared to the GHR-based CACC-ECMS and 10.47% fuel compared to the OVM-based CACC-ECMS under NEDC; (ii) 7.47% fuel compared to the GHR-based CACC-ECMS and 11% fuel compared to the OVM-based CACC-ECMS under WLTP; (iii) 3.62% fuel compared to the GHR-based CACC-ECMS and 4.22% fuel compared to the OVM-based CACC-ECMS under HWFET; and (iv) 11.05% fuel compared to the GHR-based CACC-ECMS and 18.26% fuel compared to the OVM-based CACC-ECMS under HIL.

Quantitative Evaluation of the Impacts of the Time Headway of Adaptive Cruise Control Systems on Congested Urban Freeways Using Different Car Following Models and Early Control Results

The impact of driving automation and adaptive cruise control (ACC) on traffic performance has been increasingly studied in recent years. This paper focuses on two widely used ACC car following models and investigates the impact of the time headway parameter on traffic operation and performance on one of the busiest freeway corridors in Ontario, Canada. Using Aimsun microsimulation, we compare two commonly used ACC car following models; the intelligent driver model (IDM) and Shladover’s model which has been recently adopted in Aimsun Next 20. Several experiments have been conducted to evaluate the freeway performance for different desired headway settings and market penetration rates of ACC-equipped vehicles. Simulations results confirm the reported IDM drawbacks of having a slow response leading to headway errors which are less pronounced with Shladover’s model thereby leading to more accurate quantification by the latter. This study further presents a simple on-off ACC-based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that freeway performance is improved. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average network throughput, delay, and speed compared to the case of only manually driven or uncontrolled ACC vehicles.

Comparison of Reaction Time-based Collaborative Velocity Control and Intelligent Driver Model for Agent-based Simulation of Autonomous Car

Based on historical records, driving in hazardous weather conditions is one of the most serious causes that lead to fatal accidents on roads in general and in United Arab Emirates (UAE) highways in particular. One solution to improve road safety is to equip vehicles and infrastructure with connected and smart devices and convert them into autonomous vehicles. Before deploying a concrete solution to the field, it must be validated by simulation, and more specifically by agent-based simulation. In this paper, we propose to implement the Reaction Time-Based Collaborative Velocity Control (RT-CVC) model that was implemented in autonomous cars into an agent-based simulator. This model is compared to the Intelligent Driver Model (IDM), which is one of the standard longitudinal driving behaviors in simulation environments. The experimental results show that RT-CVC generates traffic flows with fewer vehicle collisions and shorter travel times. This positive analysis is balanced by the fact that RT-CVC is designed for autonomous cars, and IDM is designed to model human-drive decisions. Using RT-CVC for modeling a human driver may be counter productive in simulation experiments.

Transfollower: Long-Sequence Car-Following Trajectory Prediction Through Transformer

Car-following refers to a control process in which the following vehicle (FV) tries to keep a safe distance between itself and the lead vehicle (LV) by adjusting its acceleration in response to the actions of the vehicle ahead. The corresponding car-following models, which describe how one vehicle follows another vehicle in the traffic flow, form the cornerstone for microscopic traffic simulation and intelligent vehicle development. One major motivation of car-following models is to replicate human drivers' longitudinal driving trajectories. To model the long-term dependency of future actions on historical driving situations, we developed a long-sequence car-following trajectory prediction model based on the attention-based Transformer model. The model follows a general format of encoder-decoder architecture. The encoder takes historical speed and spacing data as inputs and forms a mixed representation of historical driving context using multi-head self-attention. The decoder takes the future LV speed profile as input and outputs the predicted future FV speed profile in a generative way (instead of an auto-regressive way, avoiding compounding errors). Through cross-attention between encoder and decoder, the decoder learns to build a connection between historical driving and future LV speed, based on which a prediction of future FV speed can be obtained. We train and test our model with 112,597 real-world car-following events extracted from the Shanghai Naturalistic Driving Study (SH-NDS). Results show that the model outperforms the traditional intelligent driver model (IDM), a fully connected neural network model, and a long short-term memory (LSTM) based model in terms of long-sequence trajectory prediction accuracy. We also visualized the self-attention and cross-attention heatmaps to explain how the model derives its predictions.

A Hierarchical Motion Planning Framework for Autonomous Driving in Structured Highway Environments

This paper presents an efficient hierarchical motion planning framework with a long planning horizon for autonomous driving in structured environments. A 3D motion planning with time information is a non-convex problem because there exists more than one local minimum point and various constraints such as roads and obstacles. Accordingly, to deal with high computational complexity and the problem of falling into a local minimum in an enormous solution space, a decoupled method is utilized, that consists of two steps: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Long-term</i> planning and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">short-term</i> planning. First, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long-term</i> planner provides reasonable far-sighted behavior through two processes. In the first process, a rough path that includes a driving strategy is generated in the 2D spatial space. Then, the jump point search algorithm is utilized with time information on the path to reduce the computational burden of A*, giving an acceptable quality of solution at the same time. In this step, a safe, comfortable, and dynamically feasible trajectory is generated. Next, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">short-term</i> planner optimizes a short-sighted trajectory using particle swarm optimization. In this method, a steering angle set is encoded as a particle, resulting in a safe, comfortable, and kinodynamically feasible trajectory. The proposed algorithm is implemented and evaluated in a range of vehicle-in-the-loop simulation scenarios, which include various virtual static and dynamic obstacles generated by Intelligent Driver Model. In the evaluation results, the proposed method reduced the computation time by up to 0.696 s with increasing the step cost by up to about 3%. The proposed algorithm is executed every 100 ms for a planning horizon of 10 seconds, and the average computation time is 31 ms with the worst-case computation time of 94 ms.

Entangled Intelligent Driving: Relations with Automated Cars

ABSTRACT As machines become increasingly intelligent, the HCI community is presented with new challenges regarding methods to capture and understand user experience (UX). In the case of autonomous driving (AD), this involves new scenarios where humans and intelligent vehicles need to act together in real-life traffic situations with other road users. This article responds to this context by 1) outlining a longitudinal design ethnography method whereby participants drove semiautonomous cars in their everyday environments to capture such human-machine relations in real-life settings, 2) demonstrating the complexities of the relations between humans and AD vehicles, 3) engaging theories of socio-materiality and entanglement to understand the human-machine relations of AD cars, and 4) identifying anticipatory experiences that emerge from these relations and their implications for informing UX design.

Calibration of the intelligent driver model (IDM) with adaptive parameters for mixed autonomy traffic using experimental trajectory data

Autonomous vehicles (AVs) are expected and demonstrated to increase local traffic throughput and improve traffic stability. However, their car-following behaviour is not fully understood due to variations in their often black-box controllers. In this study, we calibrate the Intelligent Driver Model (IDM), as a widely used car-following model, for mixed autonomy traffic using real-world experimental trajectory data. We introduce a new variant of IDM, called adaptive IDM, by enabling real-time changes of its parameters based on prevailing traffic condition. We also include the standard deviation of velocity in the calibration objective function to capture the stop-and-go traffic behaviour. While the adaptive IDM parameters improve the AVs simulated driving behaviour, the inclusion of the standard deviation of velocity within the objective function enables reproducing the traffic oscillations observed in the experimental data. The results show that the proposed adaptive IDM and the calibration method successfully reproduce traffic patterns in mixed autonomy traffic.

Modelling integrated movements of motorcycles at urban merge sections under mixed traffic conditions

Vehicles at merge sections interact longitudinally and laterally (integrated movement). In this paper, a microscopic simulation method, based on a combination of intelligent driver model (IDM) and social force model (SFM), is developed and compared with a microscopic simulation method based solely on SFM to determine its performance in evaluating these integrated movements. From the simulation and validation results it can be seen that the proposed model (combination of IDM and SFM) is a bit more precise than the simulation model based entirely on SFM. The lateral movement of a motorcycle represents its gap-seeking behaviour: the larger the lead lateral gap between the leader vehicles, the higher the probability of lateral movement. The parameters, such as lateral distance corresponding to overtaking position, relative lane affinity, and lateral clearance between lead vehicles, determine the motorcycle filtering choice. This study is able to replicate the real-time behaviour of motorcycles.

Modeling Car-Following Behavior on Freeways Considering Driving Style

To build more accurate and realistic freeway car-following models, driving characteristics specific to freeway car following should be considered. This study, therefore, analyzed three car-following models calibrated for different driving styles. A total of 5,900 freeway car-following events were extracted from 161,055 km of driving data collected in the Shanghai Naturalistic Driving Study (SH-NDS) database. Based on the fuzzy inference system built in this study, these car-following events were categorized as representing one of two styles: nonaggressive or aggressive. The two driving styles were visualized by using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. The Gipps, Wiedemann, and intelligent driver model (IDM) car-following models were calibrated and validated for each driving style group. Using a genetic algorithm to analyze the calibrated parameters of the investigated car-following models, it was found that the model parameter values were related to driving style. When their performances were evaluated, results showed that the IDM performed best. The nonaggressive IDM and the aggressive IDM were used to simulate the car-following scenarios based on the same leading vehicle trajectories. The t-test and the F-test results showed that regarding both time gap and spacing gap, the differences of mean and variance are significant between aggressive and nonaggressive styles in nearly all of the simulated car-following scenarios. The mean spacing gap (nonaggressive: 38 m; aggressive: 30 m) and time gap (nonaggressive: 1.7 s; aggressive: 1.4 s) obtained from modeled car-following scenarios could be used directly in simulation software to show the characteristics of different driving styles.

Safety impact of cooperative adaptive cruise control vehicles’ degradation under spatial continuous communication interruption

Cooperative adaptive cruise control is recognised as an effective means to reduce the rear-end collision caused by the delayed reaction and improper driving behaviours of manually driven vehicles. However, vehicle-to-vehicle communication, as the key to collect information for the cooperative adaptive cruise control system, is likely to be interrupted by malicious attacks and equipment failures. When communication interruption occurs, sensors of the adaptive cruise control system will replace vehicle-to-vehicle communication for information collection, that is, cooperative adaptive cruise control vehicles degrade to adaptive cruise control vehicles automatically, causing an increased rear-end collision risk. This study aims to investigate the safety impact of cooperative adaptive cruise control vehicles’ degradation under spatial continuous communication interruption. A mixed platoon, consisting of cooperative adaptive cruise control vehicles, adaptive cruise control vehicles, manually driven vehicles, and improved manually driven vehicles with information-sending devices, is simulated with the realistic cooperative adaptive cruise control and adaptive cruise control model, and the intelligent driver model. Different platoon forms are discussed by adjusting the quantity and position of manually driven vehicles in the mixed platoon. The results show that manually driven vehicles can effectively block the backward extension of speed fluctuation and reduce the rear-end collision risk under the spatial continuous communication interruption. Nevertheless, equipping manually driven vehicles with information-sending devices weakens the ability to defend against communication interruption.

Analysis and comparison of traffic flow models: a new hybrid traffic flow model vs benchmark models

BackgroundThis paper compares a hybrid traffic flow model with benchmark macroscopic and microscopic models. The proposed hybrid traffic flow model may be applied considering a mixed traffic flow and is based on the combination of the macroscopic cell transmission model and the microscopic cellular automata.Modelled variablesThe hybrid model is compared against three microscopic models, namely the Krauß model, the intelligent driver model and the cellular automata, and against two macroscopic models, the Cell Transmission Model and the Cell Transmission Model with dispersion, respectively. To this end, three main applications were considered: (i) a link with a signalised junction at the end, (ii) a signalised artery, and (iii) a grid network with signalised junctions.ResultsThe numerical simulations show that the model provides acceptable results. Especially in terms of travel times, it has similar behaviour to the microscopic model. By contrast, it produces lower values of queue propagation than microscopic models (intrinsically dominated by stochastic phenomena), which are closer to the values shown by the enhanced macroscopic cell transmission model and the cell transmission model with dispersion. The validation of the model regards the analysis of the wave propagation at the boundary region.

Identifiability of car-following dynamics

The advancement of in-vehicle sensors provides abundant datasets to estimate parameters of car-following models that describe driver behaviors. The question of parameter identifiability of such models (i.e., whether it is possible to infer its unknown parameters from the experimental data) is a central system analysis question, and yet still remains open. This article presents both structural and practical parameter identifiability analysis on four common car-following models: i) the constant-time headway relative-velocity (CTH-RV) model, ii) the optimal velocity model (OV), iii) the follow-the-leader model (FTL) and iv) the intelligent driver model (IDM). The structural identifiability analysis is carried out using a differential geometry approach, which confirms that, in theory, all of the tested car-following systems are structurally locally identifiable, i.e., the parameters can be uniquely inferred under almost all initial condition and admissible inputs by observing the space gap alone. In a practical setting, we propose an optimization-based numerical direct test to determine parameter identifiability given a specific experimental setup (the specific initial conditions and input are known). The direct test conclusively finds distinct parameters under which the CTH-RV and FTL are not identifiable under the given initial condition and input trajectory.

A survey of 3D object detection algorithms for intelligent vehicles development.

With the rapid development of Artificial Intelligent algorithms on Computer Vision, 2D object detection has greatly succeeded and been applied in various industrial products. In the past several years, the accuracy of 2D object detection has been dramatically improved, even beyond the human eyes detection ability. However, there is still a limitation of 2D object detection for the applications of Intelligent Driving. A safe and reliable self-driving car needs to detect a 3D model of the around objects so that an intelligent driving car has a perception ability to real driving situations. This paper systematically surveys the development of 3D object detection methods applied to intelligent driving technology. This paper also analyzes the shortcomings of the existing 3D detection algorithms and the future development directions of 3D detection algorithms on intelligent driving.