Vehicle Lateral Displacement Research Articles

A lane-level localization method via the lateral displacement estimation model on expressway

The gradual proliferation of high-definition (HD) maps has played a pivotal role in the advancement of intelligent vehicles. However, a considerable number of vehicles still face limitations in harnessing HD maps for high-precision navigation due to the absence of lane-level localization information. To address this challenge, this paper presents a lightweight lane-level localization method based on ubiquitous low-cost on-board vehicle cameras and Global Navigation Satellite System (GNSS) receivers. Initially, the line anchor-based feature refinement network is combined with the DeepSort-based lane tracking algorithm to detect and track the lanes in the image sequences. Subsequently, a lateral displacement estimation (LDE) model is proposed to establish the relationship between the vehicle's lateral displacement value in the ego lane and the slope of the lanes on both sides. Moreover, a lateral motion estimation (LME) method is employed to track the motion of the vehicle across multiple lanes. Finally, a lane-level map matching method is proposed based on the GNSS data and lateral displacement information. In the experimental section, three distinct types of dashcams have been installed in a vehicle, and high-precision positioning equipment is utilized as the ground truth for algorithm validation. The experimental results indicate that the proposed method can be effectively deployed in vehicles, enabling lane-level vehicle positioning on expressway. The code has been open-sourced and can be accessed at https://codeocean.com/capsule/6640813/.

Multi-sensor Fusion Road Friction Coefficient Estimation During Steering with Lyapunov Method.

The road friction coefficient is a key parameter for autonomous vehicles and vehicle dynamic control. With the development of autonomous vehicles, increasingly, more environmental perception sensors are being installed on vehicles, which means that more information can be used to estimate the road friction coefficient. In this paper, a nonlinear observer aided by vehicle lateral displacement information for estimating the road friction coefficient is proposed. First, the tire brush model is modified to describe the tire characteristics more precisely in high friction conditions using tire test data. Then, on the basis of vehicle dynamics and a kinematic model, a nonlinear observer is designed, and the self-aligning torque of the wheel, lateral acceleration, and vehicle lateral displacement are used to estimate the road friction coefficient during steering. Finally, slalom tests and DLC (Double Line Change) tests in high friction conditions are conducted to verify the proposed estimation algorithm. Test results showed that the proposed method performs well during steering and the estimated road friction coefficient converges to the reference value rapidly.

Hybrid architecture for vehicle lateral collision avoidance

In this study, the authors consider the problem of collision avoidance of ground vehicles. In this framework, they focus on a single vehicle, and they guarantee that a given safe set is not exited during a mission. More specifically, they consider the yaw-lateral motion of a two-wheel vehicle subject to a side wind, and they design a state-feedback controller such that the vehicle follows the nominal trajectory with a given maximum error. For these purposes, they resort to a control system where a supervisor, implemented as a hybrid automata, decides to switch between two controllers, depending on the measured vehicle lateral displacement from the nominal trajectory. The two controllers are designed using an H ∞ approach and the input–output finite-time stability approach, respectively. Some simulation results are included, showing the effectiveness of the proposed architecture.

Linear quadratic game and non-cooperative predictive methods for potential application to modelling driver–AFS interactive steering control

This paper is concerned with the modelling of strategic interactions between the human driver and the vehicle active front steering (AFS) controller in a path-following task where the two controllers hold different target paths. The work is aimed at extending the use of mathematical models in representing driver steering behaviour in complicated driving situations. Two game theoretic approaches, namely linear quadratic game and non-cooperative model predictive control (non-cooperative MPC), are used for developing the driver–AFS interactive steering control model. For each approach, the open-loop Nash steering control solution is derived; the influences of the path-following weights, preview and control horizons, driver time delay and arm neuromuscular system (NMS) dynamics are investigated, and the CPU time consumed is recorded. It is found that the two approaches give identical time histories as well as control gains, while the non-cooperative MPC method uses much less CPU time. Specifically, it is observed that the introduction of weight on the integral of vehicle lateral displacement error helps to eliminate the steady-state path-following error; the increase in preview horizon and NMS natural frequency and the decline in time delay and NMS damping ratio improve the path-following accuracy.

Road Traffic Safety Assessment Based on the ADAMS/Car under Snow and Freezing Weather

In order to assess the traffic safety in snow and freezing weather accurately, determined the difference of vehicle lateral displacement of snow freezing and normal weather conditionsthe as the indicator, vehicle model, road model and vehicle-road coupling model were set up by using software ADAMS/Car. By changing road friction cofficient, road conditions in snow and freezing weather were simulated, step steering were carried out, vehicle lateral displacement of different speed and steering angle were obtained, the difference of vehicle lateral displacement of different speed and steering angle under snow and freezing weather and normal weather conditions were calculated, then the curve of difference of vehicle lateral displacement were generated, traffic safety level division of road under snow and freezing weather were researched based on these curves.

Integrated Driver–Vehicle–Infrastructure Road Departure Warning Unit

In this paper, a road-departure warning unit taking into account driver-vehicle-infrastructure (DVI) interactions is proposed. The longitudinal and lateral vehicle dynamics limits are analyzed to detect the road departure on loss of control. Vehicle positioning and time to lane crossing (TLC) are used to detect the road departure on a defect of guidance. Prevention of excessive longitudinal speed is handled through the computation of a critical longitudinal speed when approaching a curve and speed profile generation in the straight road section preceding the curve. For the lateral mode, the vehicle oversteering or understeering, the yaw motion, and the lateral acceleration are analyzed. The vehicle lateral displacement and the TLC values are also examined when the vehicle dynamics are not excessive. Necessary data for detection algorithms, which are not available from measurements, are estimated using an extended Kalman filter. The system consists of several subsystems, which work in parallel and provide warning through a dedicated human-machine interface (HMI). This road-departure warning system is experimentally tested on a test track using a prototype vehicle. It is found to be efficient and robust.

Estimation of arousal-level from steering operations

Analysis was carried out with respect to a large amount of data pertaining to drivers steering operations and vehicle motions on a simulator when a random side-wind input of the same level as that in actual driving conditions was applied to the vehicle. The results of this data indicated that the driver's arousal-level could be evaluated when the coherence between disturbance and steering angle or the standard deviation of the vehicle's lateral displacement was estimated. By means of computer simulation and additional testing, studies were carried out with respect to the precision of the arousal-level evaluation in situations where side-wind input and vehicle's stability factor were varied.