Lane-keeping Assistance Systems Research Articles

Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions

To enhance the control accuracy of lane-keeping assistance systems for trucks encountering crosswind-induced lateral deviations to improve the lateral stability of the vehicle, this study proposes a control strategy based on a linear quadratic regulator (LQR) using a path-tracking preview model. First, the lateral deviation is calculated using the path-tracking preview model. Then, an observer for the vehicle’s sideslip angle is designed using a vehicle lateral tracking deviation model and a Kalman filter controller, and this is used to solve the deviation of the sideslip angle. Finally, a feedforward controller is designed based on the LQR controller and a linear two-degrees-of-freedom vehicle model to eliminate steady-state errors arising from LQR optimization, thereby obtaining the steering angle of the vehicle when subjected to crosswind conditions. Comparing the test results of the sideslip angle, yaw rate, and lateral acceleration demonstrates that this strategy effectively improves the control accuracy of lane-keeping under crosswind conditions. The proposed method is validated through hardware-in-the-loop experiments on a test bench, yielding results consistent with simulations.

Ground vehicle lane-keeping assistance system via differential flatness output feedback control and algebraic derivative estimation

Vehicle run-off-road is one of the most frequent and fatal traffic accidents in the United States. Various lane-keeping assistance (LKA) systems have been developed in the last decade to help drivers stay on the road. Most of them are built upon linear driver–vehicle–road (DVR) models and treat road curvature as a disturbance. Albeit effective, their control performance would degrade if road curvature varies rapidly. This paper proposes a novel nonlinear DVR model by integrating a driver steering model into vehicle–road kinematics, which explicitly considers road curvature. Particularly, this nonlinear DVR system has been proven to be differentially flat, and a flatness-based LKA system is designed. Additionally, Model-Free Control is introduced to compensate for system modeling errors. Hardware-in-the-loop simulations and diver-in-the-loop experiments validate the proposed control framework and demonstrate the performance enhancement with respect to a representative linear robust LKA system.

Individualizable Vehicle Lane Keeping Assistance System Design: A Linear-Programming-Based Model Predictive Control Approach

Model predictive control (MPC), a control technique that can systematically and explicitly cope with system constraints, has become increasingly prevalent in the field of automotive engineering. Respecting the advanced driver-assistance systems (ADAS) design, the MPC has found its applications in various kinds of ground vehicular lane-keeping assistance systems (LKAS). In this paper, an MPC-based LKAS is synthesized by leveraging the linear programming (LP) methodology. Compared to the conventional MPC-based LKAS that is based on quadratic programming (QP), the LP alternative is less computationally demanding, which is desired for commercial vehicular electronic control units. Besides, the LP scheme enables the designer to formulate the MPC's performance criterion in the sense of L1/L∞ norms, which differs from the QP's L2-norm-based measure. The proposed LP-based MPC-LKAS is examined in CarSim-Simulink joint simulations and its performance is compared with a QP-based solution.

Safety effectiveness and performance of lane support systems for driving assistance and automation – Experimental test and logistic regression for rare events

As of 2022, lane-keeping assistance systems and other selected safety technologies will become mandatory in new European vehicles to increase safety for passengers, pedestrians and cyclists. Lane support systems (LSS) are based on advanced computer vision technologies and they are expected to give safety benefits in reducing Ran of Road and head-on crashes comparable to already consolidated physical countermeasures like rumble strips. Anyway, despite the assumed technology readiness, there is still much uncertainty regarding the needs of vision systems for “reading” the road and limited results are still available from in field testing. In such framework the paper presents an experimental test of LSS performance carried out in two lane rural roads with different geometric alignments and sections characterized by variable maintenance conditions for pavement and markings. LSS faults were detected in 2.6 % of the sections, running the roads in day light and dry pavement conditions. Logit models were developed to better understand road characteristics and conditions that can affect the system performance. The Firth penalized maximum-likelihood method was applied to estimate the logistic regression coefficients and standard errors to account for the rareness of the events. Results show that luminance coefficient of marking in diffuse lighting conditions (Qd) and horizontal curvature radius (1/R) are the main predictors of system fault. Based on the case study and test conditions, other marking characteristics, longitudinal cracking, verge width and running speed resulted not significant in explaining the probability of fault. Thresholds values for Qd and 1/R are suggested and remarks on road maintenance and design standards presented.

The Safety Potential of Lane Keeping Assistance and Possible Actions to Improve the Potential

Before widespread automated driving, driver assistance systems become more common and can deliver safety benefits. This study focuses on current and future lane keeping assistance (LKA) systems and their potential safety effects by analysing real-world crash data and LKA's possibilities to prevent fatal passenger car crashes. The analysed data includes in-depth analysis of 364 fatal head-on and single-vehicle crashes in Finland. According to statistical analysis, 27% of these crashes could potentially have been prevented had currently available LKA been deployed. In this study, many issues are examined related to the lane markings' visibility, infrastructure, weather, and driver-related risks, which rationalise the results, compliment previous research, and highlight possibilities to increase the safety potential of LKA. Related to the lane markings' visibility, safety potential could be improved by allocating more resources to road maintenance and to snow clearance. Advanced LKA enabling exploiting digital lane markings could hypothetically almost double the safety potential as weather conditions or the visibility of lane markings would not restrict LKA's operation. While the driver is still in charge of driving tasks, driver-related risks are difficult to manage and the crashes caused intendedly or due to driver's attack of illness are not preventable by current LKA systems.

An Advanced Lane-Keeping Assistance System With Switchable Assistance Modes

Lane keeping is a key task in driving, and it plays an important role in staying safe while driving. However, conventional lane-keeping assistance systems (LKASs) have a limited level of automation, while fully autonomous lane-keeping systems have shortcomings regarding reliability. To address these issues, an advanced LKAS with two switchable assistance modes, namely, the lane departure prevention mode and the lane-keeping co-pilot mode, is proposed in this paper. First, the system structure is constructed. Then, the functions and control strategies for the two assistance modes are defined. Next, the controller algorithms are designed using the learning-based model predictive control (LBMPC) method to compensate for modeling errors. Within the framework of LBMPC, an oracle is built to learn the unmodeled dynamics using an extended Kalman filter. Moreover, optimization problems are formulated to achieve the control objectives regarding driving safety and driver acceptance. Finally, driver-in-the-loop experiments carried out on a driving simulator prove that both assistance modes are effective. Furthermore, the lane-keeping co-pilot mode can help reduce the driving burden in the sense of avoiding frequent steering correction.

In-depth understanding of lane changing interactions for in-vehicle driving assistance systems

Lane-changing events are often related with safety concern and traffic operational efficiency due to complex interactions with neighboring vehicles. In particular, lane changes in stop-and-go traffic conditions are of keen interest because these events lead to higher risk of crash occurrence caused by more frequent and abrupt vehicle acceleration and deceleration. From these perspectives, in-depth understanding of lane changes would be of keen interest in developing in-vehicle driving assistance systems. The purpose of this study is to analyze vehicle interactions using vehicle trajectories and to identify factors affecting lane changes with stop-and-go traffic conditions. This study used vehicle trajectory data obtained from a segment of the US-101 freeway in Southern California, as a part of the Next Generation Simulation (NGSIM) project. Vehicle trajectories were divided into two groups; with stop-and-go and without stop-and-go traffic conditions. Binary logistic regression (BLR), a well-known technique for dealing with the binary choice condition, was adopted to establish lane-changing decision models. Regarding lane changes without stop-and-go traffic conditions, it was identified based on the odd ratio investigation that he subject vehicle driver is more likely to pay attention to the movement of vehicles ahead, regardless of vehicle positions such as current and target lanes. On the other hand, the subject vehicle driver in stop-and-go traffic conditions is more likely to be affected by vehicles traveling on the target lane when deciding lane changes. The two BLR models are adequate for lane-changing decisions in normal and stop-and-go traffic conditions with about 80 % accuracy. A possible reason for this finding is that the subject vehicle driver has a tendency to pay greater attention to avoiding sideswipe or rear-end collision with vehicles on the target lane. These findings are expected to be used for better understanding of driver’s lane changing behavior associated with congested stop-and-go traffic conditions, and give valuable insights in developing algorithms to process sensor data in designing safer lateral maneuvering assistance systems, which include, for example, blind spot detection systems (BSDS) and lane keeping assistance systems (LKAS).

Drivers’ Reliance on Lane-Keeping Assistance Systems as a Function of the Level of Assistance

We investigated drivers’ willingness to allocate control to a lane-keeping assistance system as a function of the level of assistance. Operators were found to increasingly rely on automation with higher reliability. We assume a similar effect of the level of assistance on reliance resulting from operators’ perception of a reduced need to engage in automation controlled processes. Forty five participants drove in a moving base driving simulator wiTheither a lane-keeping control (LKC) system (high level of assistance), a lane departure warning (LDW) system (low level of assistance) or without lane-keeping assistance. The allocation of visual attention away from the road scene towards an in-vehicle display of a secondary task was used as an indicator of drivers’ reliance on the systems. We found that drivers diff ered significantly in their reliance on and trust in the LKC system. Individual driver characteristics, i.e., self reported driving style, had a significant influence on drivers’ trust. Drivers placed more trust in the LKC system the more they regarded themselves as anxious and unconfident drivers, and more trust in the LDW system the more they regarded themselves as unobservant drivers. A major conclusion is that self-reported driving style is an important factor to predict if drivers are prone to place their trust in, and rely on, higher level driving assistance. Study results call for transparency in the design of driver assistance systems to avoid false expectations regarding system capabilities. Functional system limits should be made easily identifiable for drivers in order to reduce the likelihood of overreliance.

Control effects of steer-by-wire system for motorcycles on lane-keeping performance

This study discusses the control effects of the steer-by-wire (SBW) system for motorcycles on the lane-keeping performance by examining computer simulation with a rider-vehicle system which consists of a simplified vehicle model, a rider control model and the controller of the SBW system. The SBW system, which compensates the rolling angle deviation between the desired rolling angle intended by the rider and the actual rolling angle, improves the lane-keeping performance of the rider-vehicle system under the steering torque disturbance. The SBW system is, on the other hand, not effective in the lane-keeping performance under the lateral force disturbance. In addition, the lane-keeping assistance (LKA) system is applied to the SBW system and the cooperativeness of the SBW and the LKA systems is examined. The LKA system improves the lane-keeping performance of the SBW system under not only the steering torque disturbance but also the lateral force disturbance.

Online Driver Distraction Detection Using Long Short-Term Memory

Lane-keeping assistance systems for vehicles may be more acceptable to users if the assistance was adaptive to the driver's state. To adapt systems in this way, a method for detection of driver distraction is needed. Thus, we propose a novel technique for online detection of driver's distraction, modeling the long-range temporal context of driving and head tracking data. We show that long short-term memory (LSTM) recurrent neural networks enable a reliable subject-independent detection of inattention with an accuracy of up to 96.6%. Thereby, our LSTM framework significantly outperforms conventional approaches such as support vector machines (SVMs).

Negative Behavioral Adaptation to Lane-Keeping Assistance Systems

In addition to the improvement of driving comfort, modern advanced driver assistance systems also aim to substantially increase traffic safety. Meanwhile, initial optimism with respect to potential safety gains has given way to a more critical view. There is, for example, a danger that continued use is associated with drivers systematically adapting their behavior to the new systems in a negative manner. According to theoretical considerations, excessive trust in such systems may lead to the development of a tendency for the driver to delegate safety-relevant aspects of the driving task to the system, which, in cases where system limits are reached, can result in the driver and other road users being endangered. The present study examined this phenomenon in the context of lateral control assistance. In a field experiment, it was specifically investigated whether drivers who had become familiar with a heading control system developed excessive trust in the system and misjudged its limits. This question was addressed in a driving trial in which, unbeknown to the driver, the heading control system was repeatedly deactivated and the driver's behavior and lane control were observed. Analysis of subjective and objective data clearly demonstrated that drivers did not develop excessive trust in the system and accordingly regulated lateral control to a sufficient degree even after prolonged exposure to the system. Negative behavioral adaptation to the heading control system was thus not observed.

1218 二輪車の車線維持性能向上に関する研究 : ステアバイワイヤの車線維持性能への影響(OS3-1:操舵系制御,OS3:交通・物流システムの制御,オーガナイズド・セッション(OS),第18回交通・物流部門大会(TRANSLOG2009))

This study examines the effects of the Steer-By-Wire (SBW) technology on lane keeping performance of motorcycles by examining computer simulation using multibody-dynamics model. The lane keeping performance is evaluated by the rider-in-the-loop system consisting of the motorcycle, the SBW controller, and the rider control model. The SBW technology improves the lane keeping performance of motorcycles in comparison with the conventional motorcycles. In addition, the Lane-Keeping-Assistance (LKA) system is applied to the SBW motorcycle and the cooperativeness of the SBW and the LKA systems is examined. The LKA system improves the lane keeping performance of the SBW motorcycle.

Lyapunov Based Performance Guarantees for the Potential Field Lane-keeping Assistance System

Active lane-keeping assistance systems hold the potential to save thousands of lives every year, but require an approach that can simultaneously work cooperatively with the driver and provide a guaranteed level of safety. One approach that meets these dual demands is to passively couple the vehicle to the environment using the paradigm of “artificial” potential fields. This paper develops such a controller and demonstrates that with appropriate choice of a preview or look-ahead distance, the lateral and yaw dynamics effectively decouple, enabling a tight energy-based bound on lateral error. Since this control scheme does not attempt to track a desired trajectory, disturbances encountered during normal driving (such as road curvature) will alter the path of the vehicle. However, the energy theoretic framework can be used to develop a Lyapunov-based bound that handles general time-varying disturbances. This technique provides bounds on the lateral motion of the vehicle that are sufficiently tight to use as a design tool. Experimental results verify that this bound, and the system structure itself, work well in practice, providing guaranteed assistance in a cooperative manner.

Investigation of Attitude Control Assistance System for Lane-Keeping Situation

Most of Lane-Keeping Assistance Systems are designed to regulate the lateral deviation of vehicle that may interfere the driver's maneuver in some cases resulting in deterioration of driver's workload. This paper proposes a new algorithm for the Lane-Keeping Assistant System by controlling of yaw angle of vehicle as called Attitude Control Assistance System or ACAS. The ACAS controls yaw angle of vehicle using two types of control inputs, steering torque and rear steering angle in this paper. The control system is designed and the system's effectiveness is evaluated based on experiment using driving simulator.