Driver Behavior Comparison Between Static and Dynamic Simulation for Advanced Driving Maneuvers

2011 ◽  
Vol 20 (2) ◽  
pp. 143-161 ◽  
Author(s):  
B. J. Correia Grácio ◽  
M. Wentink ◽  
A. R. Valente Pais
Keyword(s):  
Paired Comparison ◽  
Driving Behavior ◽  
Driving Simulation ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Control Input ◽  
Inertial Motion ◽  
Average Speed ◽  
Motion Condition ◽  
Fixed Base

In advanced driving maneuvers, such as a slalom maneuver, it is assumed that drivers use all the available cues to optimize their driving performance. For example, in curve driving, drivers use lateral acceleration to adjust car velocity. The same result can be found in driving simulation. However, for comparable curves, drivers drove faster in fixed-base simulators than when actually driving a car. This difference in driving behavior decreases with the use of inertial motion feedback in simulators. The literature suggests that the beneficial effect of inertial cues in driving behavior increases with the difficulty of the maneuver. Therefore, for an extreme maneuver such as a fast slalom, a change in driving behavior is expected when a fixed-base condition is compared to a condition with inertial motion. It is hypothesized that driving behavior in a simulator changes when motion cues are present in extreme maneuvers. To test the hypothesis, a comparison between No-Motion and Motion car driving simulation was done, by measuring driving behavior in a fast slalom. A within-subjects design was used, with 20 subjects driving the fast slalom in both conditions. The average speed during the Motion condition was significantly lower than the average speed during the No-Motion condition. The same was found for the peak lateral acceleration generated by the car model. A power spectral density analysis performed on the steering wheel angle signal showed different control input behavior between the two experimental conditions. In addition, the results from a paired comparison showed that subjects preferred driving with motion feedback. From the lower driving speed and different control input on the steering wheel, we concluded that motion feedback led to a significant change in driving behavior.

scholarly journals Glaucomatous visual fields and neurocognitive function are independently associated with poor lane maintenance during driving simulation

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
David E. Anderson ◽  
John P. Bader ◽  
Emily A. Boes ◽  
Meghal Gagrani ◽  
Lynette M. Smith ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Vehicle Control ◽  
Driving Behavior ◽  
Driving Performance ◽  
Driving Simulation ◽  
Sensor Data ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Neurocognitive Performance ◽  
Loss Severity

Abstract Background Driving simulators are a safe alternative to on-road vehicles for studying driving behavior in glaucoma drivers. Visual field (VF) loss severity is associated with higher driving simulator crash risk, though mechanisms explaining this relationship remain unknown. Furthermore, associations between driving behavior and neurocognitive performance in glaucoma are unexplored. Here, we evaluated the hypothesis that VF loss severity and neurocognitive performance interact to influence simulated vehicle control in glaucoma drivers. Methods Glaucoma patients (n = 25) and suspects (n = 18) were recruited into the study. All had > 20/40 corrected visual acuity in each eye and were experienced field takers with at least three stable (reliability > 20%) fields over the last 2 years. Diagnosis of neurological disorder or cognitive impairment were exclusion criteria. Binocular VFs were derived from monocular Humphrey VFs to estimate a binocular VF index (OU-VFI). Montreal Cognitive Assessment (MoCA) was administered to assess global and sub-domain neurocognitive performance. National Eye Institute Visual Function Questionnaire (NEI-VFQ) was administered to assess peripheral vision and driving difficulties sub-scores. Driving performance was evaluated using a driving simulator with a 290° panoramic field of view constructed around a full-sized automotive cab. Vehicle control metrics, such as lateral acceleration variability and steering wheel variability, were calculated from vehicle sensor data while patients drove on a straight two-lane rural road. Linear mixed models were constructed to evaluate associations between driving performance and clinical characteristics. Results Patients were 9.5 years older than suspects (p = 0.015). OU-VFI in the glaucoma group ranged from 24 to 98% (85.6 ± 18.3; M ± SD). OU-VFI (p = .0066) was associated with MoCA total (p = .0066) and visuo-spatial and executive function sub-domain scores (p = .012). During driving simulation, patients showed greater steering wheel variability (p = 0.0001) and lateral acceleration variability (p < .0001) relative to suspects. Greater steering wheel variability was independently associated with OU-VFI (p = .0069), MoCA total scores (p = 0.028), and VFQ driving sub-scores (p = 0.0087), but not age (p = 0.61). Conclusions Poor vehicle control was independently associated with greater VF loss and worse neurocognitive performance, suggesting both factors contribute to information processing models of driving performance in glaucoma. Future research must demonstrate the external validity of current findings to on-road performance in glaucoma.

Lateral obstacle avoidance control based on driving behavior recognition of the preceding vehicles in adjacent lanes

2020 ◽  
Vol 68 (10) ◽  
pp. 880-892
Author(s):  
Youguo He ◽  
Xing Gong ◽  
Chaochun Yuan ◽  
Jie Shen ◽  
Yingkui Du
Keyword(s):  
Obstacle Avoidance ◽  
Lateral Displacement ◽  
Longitudinal Velocity ◽  
Driving Behavior ◽  
Lateral Acceleration ◽  
Lateral Motion ◽  
Behavior Recognition ◽  
Dynamic Surface ◽  
Lane Changing ◽  
The Impact

AbstractThis paper proposes a lateral lane change obstacle avoidance constraint control simulation algorithm based on the driving behavior recognition of the preceding vehicles in adjacent lanes. Firstly, the driving behavior of the preceding vehicles is recognized based on the Hidden Markov Model, this research uses longitudinal velocity, lateral displacement and lateral velocity as the optimal observation signals to recognize the driving behaviors including lane-keeping, left-lane-changing or right-lane-changing; Secondly, through the simulation of the dangerous cutting-in behavior of the preceding vehicles in adjacent lanes, this paper calculates the ideal front wheel steering angle according to the designed lateral acceleration in the process of obstacle avoidance, designs the vehicle lateral motion controller by combining the backstepping and Dynamic Surface Control, and the safety boundary of the lateral motion is constrained based on the Barrier Lyapunov Function; Finally, simulation model is built, and the simulation results show that the designed controller has good performance. This active safety technology effectively reduces the impact on the autonomous vehicle safety when the preceding vehicle suddenly cuts into the lane.

Study on Driver and Vehicle System With Lane-Tracking Control System Using Steering Torque Input

2001 ◽  
Author(s):  
Masao Nagai ◽  
Hidehisa Yoshida ◽  
Kiyotaka Shitamitsu ◽  
Hiroshi Mouri
Keyword(s):  
Tracking Control ◽  
High Speed ◽  
Driving Simulator ◽  
Tracking System ◽  
Torque Control ◽  
Steering Wheel ◽  
Control Input ◽  
Steering Angle ◽  
Lane Tracking ◽  
Steering Torque

Abstract Although the vast majority of lane-tracking control methods rely on the steering wheel angle as the control input, a few studies have treated methods using the steering torque as the input. When operating vehicles especially at high speed, drivers typically do not grip the steering wheel tightly to prevent the angle of the steering wheel from veering off course. This study proposes a new steering assist system for a driver not with the steering angle but the steering torque as the input and clarifies the characteristics and relative advantages of the two approaches. Then using a newly developed driving simulator, characteristics of human drivers and the lane-tracking system based on the steering torque control are investigated.

scholarly journals A Fault Tolerant Vehicle Stability Control Using Adaptive Control Allocation

2018 ◽  
Author(s):  
Ozan Temiz ◽  
Melih Cakmakci ◽  
Yildiray Yildiz
Keyword(s):  
Adaptive Control ◽  
Fault Tolerant ◽  
Stability Control ◽  
Steering Wheel ◽  
Slip Angle ◽  
Control Allocation ◽  
Control Input ◽  
Allocation Strategy ◽  
Virtual Control ◽  
Yaw Stability

This paper presents an integrated fault-tolerant adaptive control allocation strategy for four wheel frive - four wheel steering ground vehicles to increase yaw stability. Conventionally, control of brakes, motors and steering angles are handled separately. In this study, these actuators are controlled simultaneously using an adaptive control allocation strategy. The overall structure consists of two steps: At the first level, virtual control input consisting of the desired traction force, the desired moment correction and the required lateral force correction to maintain driver’s intention are calculated based on the driver’s steering and throttle input and vehicle’s side slip angle. Then, the allocation module determines the traction forces at each wheel, front steering angle correction and rear steering wheel angle, based on the virtual control input. Proposed strategy is validated using a non-linear three degree of freedom reduced two-track vehicle model and results demonstrate that the vehicle can successfully follow the reference motion while protecting yaw stability, even in the cases of device failure and changed road conditions.

Monitoring of Human Driver Behavior by Vehicle Trajectory Reconstruction for Transportation Safety Management

2013 ◽  
Vol 300-301 ◽  
pp. 589-596 ◽  
Author(s):  
Geun Sub Heo ◽  
Sang Ryong Lee ◽  
Cheol Woo Park ◽  
Moon Kyu Kwak ◽  
Choon Young Lee
Keyword(s):  
Safety Management ◽  
Lateral Acceleration ◽  
Transportation Safety ◽  
Steering Wheel ◽  
Steering Angle ◽  
Safety Control ◽  
Acceleration Signal ◽  
Human Driver ◽  
Transportation Vehicle ◽  
Reckless Driving

In this paper, we proposed a method of monitoring human driver by reconstructing trajectories which transportation vehicle followed. For safety and management of logistic transportation, it is important to monitor the states of driving behavior through the whole course of path. Since many accidents occur due to the reckless driving of the driver every year, continuous monitoring of the status of commercial vehicles is needed for safety through the entire path from start point to the destination. To monitor the reckless driving, we tried to monitor the trajectory of the vehicle by using vehicle's lateral acceleration signal. Using the correlation between steering angle and lateral acceleration, we could find the relationship between steering angle and acceleration, and finally it is possible to estimate the global direction of vehicle maneuvering. We conducted experiments to find the history of vehicle position on the curved road using Kalman Filter, and classified steering wheel condition (over-steering, and under-steering). The method is applied to central safety management system for safety control of vehicles transporting toxic gases.

Self-Steering Characteristics of FSAE Vehicle with a Linear Bicycle Model

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Prashanth Barathan ◽  
R. Aakash ◽  
Hussain Akbar ◽  
Kapilesh Kathiresh
Keyword(s):  
Critical Speed ◽  
Accurate Prediction ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Angle Variation ◽  
Dynamic Conditions ◽  
Operating Range ◽  
Bicycle Model ◽  
Steering Characteristics

A FSAE car must exhibit precise and predictable handling behaviour since it is subject to driving manoeuvres in dynamic conditions. Therefore, an accurate prediction of its self-steering characteristics becomes vitally important, especially in the expected lateral acceleration operating range. The simulation implements a linear bicycle model of FSAE car in MATLAB and establishes the understeer gradient and the critical speed, thereby aiding the analysis of the steering wheel angle variation required to negotiate the corners of increasing dynamics.

Steering Analysis of Multi-Axle Vehicle Based on ADAMS/VIEW

2012 ◽  
Vol 538-541 ◽  
pp. 2878-2881
Author(s):  
Yong Qiang Zhu ◽  
Ping Xia Zhang
Keyword(s):  
High Speed ◽  
Large Angle ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Low Speed ◽  
Vehicle Simulation ◽  
Yaw Rate ◽  
Wheel Turning ◽  
Double Phase ◽  
All Wheel Steering

In order to improve low-speed flexibility and high-speed handling and stability of multi-axle vehicle, a double-phase steering system was designed with planetary gear system. An in-phase steering mode is used when steering wheel turning in small angle. A adverse-phase steering mode is used when steering wheel turning in large angle. A five-axle vehicle simulation model was established with software ADAMS/VIEW. The research of all-wheel steering and non-all-wheel steering for high speed and low speed was respectively processed. When running in high speed, the lateral acceleration and yaw rate of the centroid are significantly lower when rear wheels steering in in-phase mode than the rear wheels not turning, which makes the possibility of roll and drift decrease, when vehicle overtaking in high-speed. When running in low speed, compared with rear wheels not steering, when rear wheels sreering, lateral acceleration increased by only 12.8%, yaw rate is 17.3% higher, diameter of the centroid trajectory is reduced by 12.9%, which greatly increases the mobility and flexibility of the multi-axle vehicle when turning at low speed.

Test Results: Vehicle Responses to Simulated Drag Caused by Front Tire Tread Detachment — The Effect of Scrub Radius and Speed

2018 ◽  
Author(s):  
Mark W. Arndt ◽  
Stephen M. Arndt
Keyword(s):  
Lateral Displacement ◽  
Rear Wheel ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Slip Angle ◽  
Yaw Rate ◽  
Wheel Drive ◽  
Steady State Responses ◽  
Four Wheel Drive ◽  
Left Front

The effects of reduced kingpin offset distance at the ground (scrub radius) and speed were evaluated under controlled test conditions simulating front tire tread detachment drag. While driving in a straight line at target speeds of 50, 60, or 70 mph with the steering wheel locked, the drag of a tire tread detachment was simulated by applying the left front brake with a pneumatic actuator. The test vehicle was a 2001 dual rear wheel four-wheel-drive Ford F350 pickup truck with an 11,500 lb. GVWR. The scrub radius was tested at the OEM distance of 125 mm (Δ = 0) and at reduced distances of 49 mm (Δ = −76) and 11 mm (Δ = −114). The average steady state responses at 70 mph with the OEM scrub radius were: steering torque = −24.5 in-lb; slip angle = −3.8 deg; lateral acceleration = −0.47 g; yaw rate = −8.9 deg/sec; lateral displacement after 0.75 seconds = 3.1 ft and lateral displacement after 1.5 seconds = 13.1 ft. At the OEM scrub radius, responses that increased linearly with speed included: slip angle (R2 = 0.84); lateral acceleration (R2 = 0.93); yaw rate (R2 = 0.73) and lateral displacement (R2 = 0.59 and R2 = 0.87, respectively). At the OEM scrub radius, steer torque decreased linearly with speed (R2 = 0.76) and longitudinal acceleration had no linear relationship with speed (R2 = 0.09). At 60 mph and 70 mph for both scrub radius reductions, statistically significant decreases (CI ≥ 95%) occurred in average responses of steer torque, slip angle, lateral acceleration, yaw rate, and lateral displacement. At 50 mph, reducing the OEM scrub radius to 11 mm resulted in statistically significant decreases (CI ≥ 95%) in average responses of steer torque, lateral acceleration, yaw rate and lateral displacement. At 50 mph the average slip angle response decreased (CI = 87%) when the OEM scrub radius was reduced to 11 mm.

Stability of Controlled Road Vehicles: A Preliminary Fundamental Study

2015 ◽  
Author(s):  
Fabio della Rossa ◽  
Massimiliano Gobbi ◽  
Giampiero Mastinu ◽  
Carlo Piccardi ◽  
Giorgio Previati
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Preliminary Investigation ◽  
Basin Of Attraction ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Vehicle Model ◽  
The Stability ◽  
Positive Effect

A comparison of the lateral stability behaviour between an autonomous vehicle, a vehicle with driver and a vehicle without driver (fixed steering wheel) is made by introducing a simple mathematical model of a vehicle running on even road. The mechanical model of the vehicle has two degrees of freedom and the related equations of motion contain the nonlinear tyre characteristics. The driver is described by a well-known model proposed in the literature. The autonomous vehicle has a virtual driver (robot) that behaves substantially like a human, but with its proper reaction time and gain. The road vehicle model has been validated. The study of vehicle stability has to be based on bifurcation analysis and a preliminary investigation is proposed here. The accurate computation of steady-state equilibria is crucial to study the stability of the three kinds of vehicles here compared. The stability of the bare vehicle without driver (fixed steering wheel) is studied in a rather complete way referring to a number of combinations of tyre characteristics. The (known) conclusion is that the understeering vehicle is stable at each lateral acceleration level and at each vehicle speed. The additional (partially unknown) conclusion is that the vehicle (model) with degradated tyres may exhibit a huge number of different bifurcations. The driver has many effects on the stability of the vehicle. One positive effect is to eliminate the many possible different equilibria of the bare vehicle and keep active one single equilibrium only. Another positive effect is to broaden the basin of attraction of stable equilibria (at least at relatively low speed). A negative effect is that, even for straight running, the driver seem introducing a subcritical Hopf bifurcation which limits the maximum forward speed of some understeering vehicles (that could run faster with fixed steering wheel). Both the mentioned positive and negative effects appear to be applicable to autonomous vehicles as well. Further studies could be useful to overcome the limitations on the stability of current autonomous vehicles that have been identified in the present research.

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