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Vehicle-to-everything (V2X) technology will significantly enhance the information perception ability of drivers and assist them in optimizing car-following behavior. Utilizing V2X technology, drivers could obtain motion state information of the front vehicle, non-neighboring front vehicle, and front vehicles in the adjacent lanes (these vehicles are collectively referred to as generalized preceding vehicles in this research). However, understanding of the impact exerted by the above information on car-following behavior and traffic flow is limited. In this paper, a car-following model considering the average velocity of generalized preceding vehicles (GPV) is proposed to explore the impact and then calibrated with the next generation simulation (NGSIM) data utilizing the genetic algorithm. The neutral stability condition of the model is derived via linear stability analysis. Numerical simulation on the starting, braking and disturbance propagation process is implemented to further study features of the established model and traffic flow stability. Research results suggest that the fitting accuracy of the GPV model is 40.497% higher than the full velocity difference (FVD) model. Good agreement between the theoretical analysis and the numerical simulation reveals that motion state information of GPV can stabilize traffic flow of following vehicles and thus alleviate traffic congestion.

This paper deals with longitudinal collision avoidance systems (CAS) where the target is a preceding vehicle in the same lane. It examines, in behavioral terms, the best way of giving CAS support in conditions that confront drivers with problems of reduced visibility while car following. It elaborates on earlier work on CAS support by asking whether the design of a CAS should be indifferent to varying external conditions, or whether it should be adapted to those conditions by introducing new parameter settings. The logic of the experiment rests on some assumptions that are presented before describing the experiment itself.

Advanced driver assistance system such as adaptive cruise control, traffic jam assistance, and collision warning has been developed to reduce the driving burden and increase driving comfort in the car-following situation. These systems provide automated longitudinal driving to ensure safety and driving performance to satisfy unspecified individuals. However, drivers can feel a sense of heterogeneity when autonomous longitudinal control is performed by a general speed planning algorithm. In order to solve heterogeneity, a speed planning algorithm that reflects individual driving behavior is required to guarantee harmony with the intention of the driver. In this paper, we proposed a personalized longitudinal driving system in a car-following situation, which mimics personal driving behavior. The system is structured by a multi-layer framework composed of a speed planner and driver parameter manager. The speed planner generates an optimal speed profile by parametric cost function and constraints that imply driver characteristics. Furthermore, driver parameters are determined by the driver parameter manager according to individual driving behavior based on real driving data. The proposed algorithm was validated through driving simulation. The results show that the proposed algorithm mimics the driving style of an actual driver while maintaining safety against collisions with the preceding vehicle.

This article constructs a simplified lane-changing scene model. Frequent lane-changing in actual driving scenarios will reduce driving safety and comfort. This article uses a gain function to analyze the generation of lane-changing intentions and rely on the minimum safety distance formula to judge The risk of changing lanes. When the relevant conditions are met, the vehicle executes the lane-changing action, otherwise it continues to maintain the car following state. In this paper, the lane-changing trajectory is generated based on the general model of the fifth degree polynomial, and the relatively optimal lane-changing trajectory is selected by calculating the value of the loss function. Finally, it is verified and analyzed through the MATLAB simulation environment. The results show that the method proposed in this paper has achieved a significant improvement in the safety and stability of the lane changing action.

This study proposes a novel car-following (CF) model in mixed traffic environment. The mixed traffic flow in this study incorporate with human-driven vehicles (HDVs) and connected vehicles(CVs). Then these differences of the delay of perception and sensitivity coefficient of velocity difference between HDVs and CVs are considered to set up the model. Then the perturbation method is used to testify the reliability of the proposed CF model. In addition, a sequence of numerical experiments also carried out. By analyzing the influence of the adoption degree of V2V information and the occupancy rate of CVs on the stability, the results show the availability of the suggested CF model.

Computer-driven vehicles will behave differently from human-driven vehicles due to changes in perception abilities, precision control, and reaction times. These changes are expected to have profound impacts on capacity, yet few models of automated driving are based on empirical measurements of computer-driven vehicles in real traffic. To this end, this paper investigates characteristics of an early form of longitudinal control automation, a commercially available adaptive cruise control (ACC) system driven in real traffic. Two car-following models were calibrated to a vehicle with ACC. First, the Intelligent Driver Model was reformulated to comply with ACC design standards then calibrated to match speed and range data from the test vehicle. The vehicle with ACC was found to decelerate less severely than predicted by the model when tested in severe braking and unimpeded acceleration scenarios. Second, the Wiedemann 99 model was calibrated because it is the default car-following model in the traffic microsimulation software program Vissim and can therefore be implemented cheaply and quickly in sophisticated models of roadways worldwide. Four parameters of the Wiedemann 99 model were measured directly from field observations of the test vehicle: standstill distance, start-up time, unimpeded acceleration profile, and maximum desired deceleration. Simulation results in Vissim were found to match the adaptive cruise control in unimpeded acceleration tests. These findings will benefit researchers and modelers seeking more accurate models of car-following behavior with adaptive cruise control and automated longitudinal control.

With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior&rsquo, s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers&rsquo, behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.

Adaptive Cruise Control (ACC) is an Advanced Driver Assistance System (ADAS) that enables vehicle following with desired inter-vehicular distances. Cooperative Adaptive Cruise Control (CACC) is upgraded ACC that utilizes additional inter-vehicular wireless communication to share vehicle states such as acceleration to enable shorter gap following. Both ACC and CACC rely on range sensors such as radar to obtain the actual inter-vehicular distance for gap-keeping control. The range sensor may lose detection of the target, the preceding vehicle, on curvy roads or steep hills due to limited angle of view. Unfavourable weather conditions, target selection failure, or hardware issue may also result in target detection loss. During target detection loss, the vehicle following system usually falls back to Cruise Control (CC) wherein the follower vehicle maintains a constant speed. In this work, we propose an alternative way to obtain the inter-vehicular distance during target detection loss to continue vehicle following. The proposed algorithm integrates inter-vehicular communication, accurate vehicle localization, and a digital map with lane center information to approximate the inter-vehicular distance. In-lab robot following experiments demonstrated that the proposed algorithm provided desirable inter-vehicular distance approximation. Although the algorithm is intended for vehicle following application, it can also be used for other scenarios that demand vehicles' relative distance approximation. The work also showcases our in-lab development effort of robotic emulation of traffic for connected and automated vehicles.

This paper proposes an optimal car-following strategy for pure electric vehicles (EVs) with the aim of keeping an expected headway of the leader and reducing vehicle battery loss. In particular, a car-following system model is established. The primary task of the automatic vehicle is to follow the trajectory of the preceding car and maintain an expected headway. Then, the paper analyzes the powertrain of the electric vehicle. The loss of battery life over a period of time is proportional to the acceleration, so it takes the battery life into consideration. The Q-learning algorithm is conducted for the optimal car-following strategy using system data instead of system dynamics information. It utilizes reward function and greedy strategy to select actions to train the following vehicle to achieve car-following safety. When there is no collision in these two cars, acceleration is considered into reward function to reduce battery loss. Finally, it is verified by simulation that the proposed car-following strategy can keep good tracking, maintain the expected headway from the preceding vehicle, and reduce battery loss.

Platooning/car following has been considered as a promising approach for improving vehicle efficiency due to the reduction of aerodynamic force when closely following a pilot vehicle. However, safety is a major concern in the close car platooning/following. This paper investigates the minimum inter-vehicle distances required for a passenger vehicle to safely travel behind a heavy-duty truck with three different types of emergency maneuvers. The three emergency maneuvers considered are braking only, steering only, and braking then steering, where steering refers to a single lane change maneuver. Numerical analysis is conducted for deriving the clearance space in the braking only scenario. In addition, simulations are conducted in MATLAB/Simulink, using a bicycle model for the vehicle dynamics, to examine the minimum safe following distance for the other two scenarios. The simulation results show that, for initial vehicle speeds greater than 8 m/s, a lane change maneuver requires the shortest safety distance. Braking followed by lane changing usually requires the largest minimum safety distance.

Prediction of driver reaction to the lead vehicle motion based on the perception-reaction time (PRT) is critical for prediction of rear-end crash risk. This study determines PRT at various spacings in approaching and braking conditions, and examines the association of PRT and deceleration rate with crash risk. For these tasks, a total of 50 drivers' behavior was observed in a driving simulator experiment with 4 different scenarios - reaction to a decelerating lead vehicle, reaction to a stopped lead vehicle, perception of a lead vehicle's speed change, and perception of a slow-moving lead vehicle. The study tested three hypotheses of PRT including perception and reaction thresholds and the evidence accumulation framework using a visual variable (tau-inverse). It was found that the drivers neither reacted after a specific PRT from the start of perception nor reacted at a specific value of tau-inverse. Rather, the drivers generally reacted when the accumulation of evidence (tau-inverse) over time reached a threshold. It was also found that the magnitude of deceleration rate depends on the tau-inverse at the start of braking and hence, higher crash risk was associated with higher level of urgency and insufficient brake force rather than longer PRT. This study demonstrates that the evidence accumulation framework is a promising method of predicting driver reaction in approaching and braking conditions for different types of lead vehicle, and the level of urgency is important for predicting the probability of crash.

This study evaluates the effects of various market penetration rates of connected autonomous vehicles (CAV) on traffic operations at roundabouts. We have utilized a simulation-and an optimization-based approach for this purpose. The simulation-based approach included calibrated car following models with different driving behavior parameters for a mixed fleet of conventional vehicles and CAVs with various penetration rates ranging from 0% to 100%. We also used an optimization-based approach for the 100% CAV market penetration rate case to evaluate operations while trajectories of CAVs were optimized by a central controller. The simulation results showed that CAVs improved traffic operations in under-and semi-saturated flow conditions. Nevertheless, the optimization of CAV trajectories resulted in significant delay reductions and improvements in the roundabout performance. These results indicate that CAVs have great potentials for improving traffic operations once an effective control algorithm is available.

In recent years, much emphasis has been placed on developing driving strategies for a small number of autonomous vehicles (AVs) or even adaptive cruise control (ACC) vehicles to stabilize traffic flow and reduce traffic oscillations. Many of these strategies rely on more passive car following behavior of the AV, or larger time gaps for the AV than the human-driven traffic. A common criticism of these driving strategies is that they encourage human drivers to cut in in front of the AV, which induces additional oscillations. However, until now, this claim has been largely untested. Specifically, it is unclear how the increase in oscillatory traffic conditions as a result of increased cut-ins compares to the increase that results from poor car following behavior. This study presents a simple, simulationbased analysis to answer this. A large vehicle trajectory database recorded in real traffic is analysed to understand the characteristics of typical cut-ins, and these cut-ins are simulated using common car following models for human-driven and ACC vehicles. We find that for typical cut-ins, poor driving behavior of human drivers increases oscillations by roughly 156%, while increased cut-ins increases oscillations by only 77%. Thus, we conclude that AV or ACC driving strategies that stabilize the traffic flow by engaging in more passive car following behavior than the more aggressive human drivers can still be beneficial, even if it induces additional cut-in maneuvers.

Today’s vehicles are capable of detecting environmental traffic participants, such as other vehicles, pedestrians, traffic lights etc, and communicating with each other or infrastructures. Typical on-board detectors include LiDAR, camera and so on. These vehicles which can make driving decisions based on the detected information without human intervention are named CAV (connected and autonomous vehicles). However, in a long period, the road traffic is mixed by traditional vehicles (human driven vehicles, or HVs) and CAV. The system can only “see” the near field vehicles around the CAVs by means of on-board detectors or VANET (vehicular ad hoc network). Far-field vehicles are either too far away or covered by near-field vehicles. In order to enhance the sensing capabilities of VANET or CAV, the manuscript propose a far-field vehicles sensing method, called F2-sensing. The method combines the deep learning and the car following logic. The rationale is that, as the vehicles react to downstream vehicles’ states variation, when the CAVs and the near field vehicles’ states are known, the downstream vehicles’ existence and its real-time location can be estimated. The proposed method is tested against real world dataset, which proves the usefulness of the method.

Car-following is a fundamental operation for vehicle control for both ADAS on modern vehicles and vehilce control on autonomous vehicles. Most existing car following mechanisms react to the observations of nearby vehicles in real-time. Unfortunately, lack of capability of taking into account multiple constraints and objectives, these mechanisms lead to poor efficiency, discomfort, and unsafe operations. In this paper, we design and implement a proactive car-following model to take into account safety regulation, efficiency, and comfort using deep reinforcement learning. The evaluation results show that the proactive model not only reduces the number of inefficient and unsafe headway but also eliminates the traffic jerk, compared to human drivers. The model outperformed 79% human drivers in public data set and the road efficiency is only 2% less than the optimal bound. Compared to ACC model, the DDPG model allows 4.4% more vehicles to finish the simulation than ACC model does, and increases the average speed for 28.4%.

Low illumination is a special driving condition that negatively affects drivers' vision, information acquisition (IA) ability, distance recognition and risk perception. This study evaluated drivers' car-following behaviours and characteristics using questionnaire-based research conducted among 214 drivers in Hefei. In this study, exploratory factor analysis (EFA) was used to determine the factor structure of the scale, and the internal consistency of all factors was good. The results show that low illumination strongly influences drivers' following behaviour and that they tend to choose safe and conservative ways to follow leading vehicles. Street lights are beneficial, aiding drivers' IA and their grasp of surrounding or distant environments. Myopic drivers performed worse in car following when driving in a low illumination environment, regardless of the presence of street lights. Drivers with astigmatism performed worse when street lights were present. Drivers who reported more aberrant behaviours were more aggressive when driving and tended to adopt shorter following distances at night.

The car-following behavior has attracted much attention in past decades. However, the majority of the existing studies ignored the fact that the following vehicle in car-following may prevent the lane-changing of the vehicle on the adjacent lanes, when a large gap exists between the following and leading vehicles. Therefore, this paper proposes a new car-following model considering the lane-changing prevention effect. The final velocity of the following vehicle is a combination of a safe velocity and a lane-changing prevention velocity. The stability condition of the model is derived and verified through numerical simulation, and impacts of several factors on stability are analyzed. The results display that the stability condition is consistent with the simulation results. The most significant factors impacting on the stability are the safe time-headway for lane-changing and the contribution proportion α of the safe velocity and lane-changing prevention velocity. The optimal values exist for the proportion α and lane-changing time headway that can make the stability of the traffic flow the highest.

The categorization analysis of car-following behaviors is beneficial to enrich the current car-following models and the applications of connected and automated vehicles (CAVs) in a mixed tr...

We offer the hypothesis that a variation in the visual environment of a driver affects their performance in estimating the speed of a car in front. The hypothesis was tested in a driving simulator with 18 drivers by recording their ability to estimate the relative speed of a car ahead when exposed to sudden variations in the visual environment. The sudden variation was produced by briefly (200 ms) masking the driving environment with a grey frame. The results of our study confirm the hypothesis, as the flashed mask significantly lowered the drivers' accuracy in estimating the speed of a car ahead. The results also show that it is possible to cope with variations in the visual environment and to partially recover from the loss of accuracy. The findings are relevant to the layout of driving environments, such as the placement of dynamic advertisements along the side of the road or the entrance zones of tunnels, and to the training of drivers.

Part 6: Pattern Recognition; International audience; In this paper, Analysis of the stability and solitary waves for a car-following model on two lanes is carried out. The stability condition of the model is obtained by using the linear stability theory. We study the nonlinear characteristics of the model and obtain the solutions of Burgers equation, KDV equation, and MKDV equation, which can be used to describe density waves in three regions (i.e., stable, metastable and unstable), respectively. The analytical results show that traffic flow can be stabilized further by incorporating the effects come from the leading car of the nearest car on neighbor lane into car-following model.

The behavior of the optimal velocity model is investigated in this paper. Both deterministic and stochastic perturbations are considered in the Optimal velocity model and the behavior of the dynamical systems and their convergence to their associated averaged problems is studied in detail.

This study was motivated by the work of researchers who conducted field experiments on roadways with no bottlenecks. The ring roads are circular roadways where passing was restricted. All d...

Traffic flow models have been the subject of extensive studies for decades. The interest in these models is both as the result of their important applications as well as their complex behavior which makes them theoretically challenging. In this paper, an optimal velocity dynamical model is considered and analyzed. We consider a dynamical model in the presence of perturbation and show that not only such a perturbed system converges to an averaged problem, but also we can show its order of convergence. Such understanding is important from different aspects, and in particular, it shows how well we can approximate a perturbed system with its associated averaged problem.

In this research, we present an algorithm-optimized car-following model to describe the overall effect on the Chengdu ring expressway (National Expressway G4202) in China from May to October, 2019, based on dynamic data observed from our intelligent transportation system. To validate the feasibility of our model, stability analysis is performed to obtain the critical stability condition of local traffic system, and numerical simulation is also carried out to illustrate that uncertainty of accelerated velocity of individual vehicles can influence these vehicles starting process and traffic flow stability essentially.

This paper introduces a cellular automaton design of intersections and defines rules to model traffic flow through them, so that urban traffic can be simulated. The model is able to simulate an intersection of up to four streets crossing. Each street can have a variable number of lanes. Furthermore, each lane can serve multiple purposes at the same time, like allowing vehicles to keep going straight or turn left and/or right. The model also allows the simulation of intersections with or without traffic lights and slip lanes. A comparison to multiple empirical intersection traffic data shows that the model is able to realistically reproduce traffic flow through an intersection. In particular, car following times in free flow and the required time value for drivers that turn within the intersection or go straight through it are reproduced. At the same time, important empirical jam characteristics are retained.

In intelligent transportation systems, vehicles can obtain more information, and the interactivity between vehicles can be improved. Therefore, it is necessary to study car-following behavior during the introduction of intelligent traffic information technology. To study the impacts of drivers&rsquo, characteristics on the dynamic characteristics of car-following behavior in a vehicle-to-vehicle (V2V) communication environment, we first analyzed the relationship between drivers&rsquo, characteristics and the following car&rsquo, s optimal velocity using vehicle trajectory data via the grey relational analysis method and then presented a new optimal velocity function (OVF). The boundary conditions of the new OVF were analyzed theoretically, and the results showed that the new OVF can better describe drivers&rsquo, characteristics than the traditional OVF. Subsequently, we proposed an extended car-following model by combining V2V communication based on the new OVF and previous car-following models. Finally, numerical simulations were carried out to explore the effect of drivers&rsquo, characteristics on car-following behavior and fuel economy of vehicles, and the results indicated that the proposed model can improve vehicles&rsquo, mobility, safety, fuel consumption, and emissions in different traffic scenarios. In conclusion, the performance of traffic flow was improved by taking drivers&rsquo, characteristics into account under the V2V communication situation for car-following theory.

This study aims to explore the effects of human panic factor on drivers' driving behavior. Most of the car following models focus on idealistic situations aiming for perfection, traffic psychology, however, suggests that emotions do play a significant role in drivers' behavior which in result effect their driving and decision making. Therefore, it is necessary to incorporate human factors in car following models for better realistic results in driving situations where external task demand increases (for example, poor weather conditions like fog, or making up to a meeting in time). Despite the fact that car following models have sublime appreciation in literature, none of them has focused on incorporating human panic factor in these models. Although some work is being done on understanding panic factor in drivers which helps us to understand their driving behaviors and effect on acceleration under panic situations, but this work is limited to statistical approach. This study is intended to fill this void by reviewing literature and making latest advancements by integrating human panic factor in Intelligent Driver Model (IDM). We attempted to integrate human panic factor in IDM, and simulation-based results verified our assumptions for the enhanced version of IDM. The enhanced version of model namely P-IDM models the acceleration behavior of drivers under panic condition, and reproduces acceleration as intended.

Driver and pedestrian behaviour significantly affect the safety and the flow of traffic at the microscopic and macroscopic levels. The driver behaviour models describe the driver decisions made in different traffic flow conditions. Modelling the pedestrian behaviour plays an essential role in the analysis of pedestrian flows in the areas such as public transit terminals, pedestrian zones, evacuations, etc. Driver behaviour models, integrated into simulation tools, can be divided into car-following models and lane-changing models. The simulation tools are used to replicate traffic flows and infer certain regularities. Particular model parameters must be appropriately calibrated to approximate the realistic traffic flow conditions. This paper describes the existing car-following models, lane-changing models, and pedestrian behaviour models. Further, it underlines the importance of calibrating the parameters of microsimulation models to replicate realistic traffic flow conditions and sets the guidelines for future research related to the development of new models and the improvement of the existing ones.

In this paper, an extended car-following model with consideration of the driver’s desire for smooth driving and the self-stabilizing control in historical velocity data is constructed. Moreover, for better reflecting the reality, we also integrate the velocity uncertainty into the new model to analyze the internal characteristics of traffic flow in situation where the historical velocity data are uncertain. Then, the model’s linear stability condition is inferred by utilizing linear stability analysis, and the modified Korteweg-de Vries (mKdV) equation is also obtained to depict the evolution properties of traffic congestion. According to the theoretical analysis, we observe that the degree of traffic congestion is alleviated when the control signal is considered, and the historical time gap and the velocity uncertainty also play a role in affecting the stability of traffic flow. Finally, some numerical simulation experiments are implemented and the experiments’ results demonstrate that the control signals including the self-stabilizing control, the driver’s desire for smooth driving, the historical time gap, and the velocity uncertainty are of avail to improve the traffic jam, which are consistent with the theoretical analytical results.

The area of traffic flow modelling and analysis that bridges civil engineering, computer science, and mathematics has gained significant momentum in the urban areas due to increasing vehicular population causing traffic congestion and accidents. Notably, the existence of mixed traffic conditions has been proven to be a significant contributor to road accidents and congestion. The interaction of vehicles takes place in both lateral and longitudinal directions, giving rise to a two-dimensional (2D) traffic behaviour. This behaviour contradicts with the traditional car-following (CF) or one-dimensional (1D) lane-based traffic flow. Existing one-dimensional CF models did the inclusion of lane changing and overtaking behaviour of the mixed traffic stream with specific alterations. However, these parameters cannot describe the continuous lateral manoeuvre of mixed traffic flow. This review focuses on all the significant contributions made by 2D models in evaluating the lateral and longitudinal vehicle behaviour simultaneously. The accommodation of vehicle heterogeneity into the car-following models (homogeneous traffic models) is discussed in detail, along with their shortcomings and research gaps. Also, the review of commercially existing microscopic traffic simulation frameworks built to evaluate real-world traffic scenario are presented. This review identified various vehicle parameters adopted by existing CF models and whether the current 2D traffic models developed from CF models effectively captured the vehicle behaviour in mixed traffic conditions. Findings of this study are outlined at the end.

Nowadays, automated driving is one of the most discussed topic in transportation research community and media. Although several studies demonstrated that automated driving could improve road safety and operations, other evidences underscore the emerging nature of this technology and suggest that still much more research is needed before widespread benefits can be realized. One concern is surely related to the understanding if an automation period can reduce fatigue and/or distract drivers, especially when they have been inattentive and involved in a secondary task during highly automated driving. The aim of this study is to assess the driver behaviour after resuming control from a highly automated vehicle. A driving simulator study was designed and forty-three participants drove twice a highway scenario. One drive was without automation, just manual control of the vehicle (FM). In the other drive, the automation was activated in the first half of the drive and the drivers were asked to watch a movie inside the vehicle; then they resumed control from the automation and drove manually the second half of the drive (AM). In both the manual control drives, several expected (car following and passing) and unexpected (sudden brake of leading vehicle) events occurred. Several driving performance were collected, analysed and compared between the two drives for each event. Moreover, subjective measures were also collected by means of NASA-TLX questionnaire to evaluate the workload perceived while driving. The results does not show significant after-effects of the automation on driving performance, although a more dangerous behaviour of drivers who previously had a driving automation period was noted in some cases.