Driver Steering Support Interfaces Near the Vehicle’s Handling Limits

Abstract

The goal of this thesis is to propose steering support systems that can reduce the driver’s control effort, mental load and promote safety. The driver dictates the vehicle’s motion and the support should centralize him/her in the control loop; thus our design philosophy is to increase driver’s responsibility and support him/her in the sense of information rather than automation. Incarnating such an abstract theme into a concrete problem which can be methodologically solved in terms of engineering science, necessitates a milestone-oriented work approach. Thus, the path to realize this development is to systematically sub-divide the concept into distinct milestones allowing to embody this high-level idea into objectively assessed steering interfaces. This milestone-oriented approach can be divided into seven steps: i) Study the state-of-the-art driver support systems and identify the potential space for improvement. ii) Develop the means (driving simulators, vehicular instrumentation and data analysis methods) to aid the driver steering support interface research. iii) Study the driver steering interface without any support. iv) Utilize the gathered knowledge to develop steering support interfaces, assess them in simulation level, v) and adapt the simulation support controllers into real vehicles and test them. vi) Evaluate the influence of the support interface with the real vehicle results. vii) Based upon the assessment, make a road-map for the commercial implementation of the support interface; if it is fruitful promote its further development with ultimate goal the adoption into production vehicles. he aforementioned milestone-oriented approach has been followed for the development of the driver steering support interfaces presented in this thesis. The current summary substantiates the milestones into the distinct goal addressed in Chapters 2 – 7. The goal to develop the hardware and performance evaluation-control methods in order to engineer realistic haptic cues on the steering wheel of our driving simulator is addressed in Chapter 2. A relatively low-cost solution for hardware is deployed, consisting of a velocity-controlled three-phase brushless servomotor, whose high bandwidth control allows for a realistic representation of forces. To test the system, different inertia-spring-damper systems were simulated and evaluated in time and frequency domain. We concluded, that the designed system allowed reproduction of a large range of steering wheel dynamics and forces, comparable to those found in actual cars. Our target to systematically adjust the steering systems properties of the driving simulator so that it matches the steering feedback and vehicle response of a certain vehicle is addressed in Chapter 3. To do so, we employed the steering sensitivity and steering torque gradient, which are two important metrics describing on-centre vehicle dynamics response and steering feedback. We acquired the steering metrics of real cars during double-lane change tests and indicated the key parameters of the vehicle that determine these steering metrics. We instrumented and tested five modern passenger cars, and used a vehicle dynamics model to extract the metrics for multiple vehicular parameterizations (steering ratio, power assist level, etc.) and test speeds. Sensitivity analysis showed that steering sensitivity was mainly influenced by the components that determine the steering ratio whereas the steering torque gradient was also affected by power assist steering settings. By completing this work, we had the foundation to easily assess the realism of our simulated vehicles’ response as well as to easily adapt the vehicular settings to achieve a realistic steering feedback in our driving simulator. Lane departure appears relevant in 179,000 crashes per year and is related to the greatest number of fatal crashes; up to 7,500 fatal crashes per year in the United States. Infiniti predicts that if lane departure prevention (LDP) were fitted to all vehicles, some 12% of all road fatalities could be prevented annually. The problem is that although numerous studies have shown the potential of lane keeping and LDP systems, there are few studies related to their effects during emergency manoeuvres. Thus, Chapter 4 aims to investigate a road-departure prevention (RDP) system during an emergency manoeuvre. We present a driving-simulator experiment which evaluated various steering interfaces of a road-departure prevention (RDP) system in an emergency situation. The interfaces were: 1) haptic-feedback (HF) where the RDP provided advisory steering torque; 2) drive-by-wire (DBW) where the RDP automatically corrected the front-wheel angle; and 3) DBW & HF, which combined both setups. The RDP system intervenes by applying haptic (guidance) feedback torque and/or correcting the angle of the front wheels (drive-by-wire) when road departure is likely to occur. Thirty test drivers tried to avoid an obstacle (a pylon-confined area) while keeping the vehicle on the road. The results showed that HF without DBW had a significant impact on the measured steering torque, but no significant effect on steering-wheel angle or vehicle path. DBW prevented road departure and reduced mental workload, but lead to inadvertent human-initiated counter-steering. It was concluded that a low level of automation, in the form of HF, does not prevent road departures in an emergency situation. A high level of automation, on the other hand, is highly effective in preventing road departures. Chapter 5 has been divided into three parts (A, B, C), all related to real vehicle testing. Our goal to construct a versatile low-cost instrumentation suitable to be fitted on race cars and develop the methods for processing from raw measurements to user-friendly data suitable for driver behaviour studies is addressed in part A. Through a case study on driving behaviour, during the execution of high speed skid-pad manoeuvres, we could easily notice the markedly different driving behaviours between an expert and a novice driver. The experienced driver could learn quickly how to perform repeatable trajectories, unlike the novice driver. The consistently high performance of the expert driver was realized by relatively small correcting inputs (steering wheel angle, throttle). The experienced driver was able to quickly learn how to generate the correct inputs to the vehicle, to yield repeatable vehicle behaviour and consistently perform well. Our aim to investigate driver control actions during high speed cornering with a rear wheel drive vehicle is depicted in Chapter 5, part B. Six drivers were instructed to perform the fastest manoeuvres possible around a marked circle, while trying to retain control of the vehicle and constant turning radius. The data reveal that stabilization of the vehicle is achieved with a combination of steering and throttle regulation. The results show that the drivers used steering control to compensate for disturbances in yaw rate and sideslip angle. Vehicle accustomed drivers had the most consistent performance resulting in reduced variance of task metrics and control inputs. Our target to design controllers that can stabilize the vehicle as an expert driver would is approached in part C of Chapter 5. There, we present data of driver control commands and vehicle response during the execution of cornering manoeuvres at high sideslip angles (drifting) by an expert driver using a RWD vehicle. The data reveal that stabilization of the vehicle with respect to such cornering equilibria requires a combination of steering and throttle regulation. A four wheel vehicle model with nonlinear tire characteristics is introduced and the steady-state drifting conditions are solved numerically to derive the corresponding control inputs. A sliding mode control is proposed to stabilize the vehicle model with respect to steady-state drifting, using steering angle and drive torque inputs. The performance of the controller is validated in a high fidelity simulation environment; the controller can stabilize the vehicle similarly to an expert driver. We also conceptually describe how the proposed controller can motivate a driver steering support drifting interface in the by-wire sense. Our goal to objectively evaluate vehicular steering systems through detailed driver models is substantiated in Chapter 6. It presents a driver model that consists of a preview controller part that responds to visual feedback and a neuromuscular component that reacts to force-feedback. The developed model is sensitive to steering wheel systems with different dynamics, and can predict both goal-directed steering wheel movements, as well as neuromuscular feedback. To provide evidence, we simulated different parameterizations of a steering system and tested them in conjunction with the developed driver model. We concluded that the developed model could predict the expected response for different steering setups. Our milestone goal to propose haptic steering wheel support when driving near the vehicle’s handling limit (Haptic Support Near the Limits: HSNL) is addressed in Chapter 7. The rationale behind the HSNL, derives from the vehicle’s property to reduce the steering “stiffness” (the steering feedback torque as a function of the steering wheel angle) before the vehicle reaches its handling limits and starts to understeer. The HSNL exaggerates the reduction of the steering “stiffness” and makes it profound to the driver, so he/she avoids excessive steering angle inputs which will result in increased tire slip and consequently lateral force loss. Chapter 7 is divided into two parts (A, B). Part A of Chapter 7 studies the influence of the HSNL in (a) driver-in-the-loop simulation and in (b) real track testing with a vehicle (Opel Astra G/B) equipped with a variable steering feedback torque system. In the simulator study (a) 25 drivers attempted to achieve maximum velocity, on a dry skid-pad while trying to retain control of the simulated vehicle parameterized as the Astra. In (b) 17 drivers attempted to achieve maximum velocity, around a wet skid-pad while trying to retain control of the Astra. Driving aids (ABS and traction control) were disabled during testing. Both the driving simulator and the real vehicle tests led to the conclusion that HSNL assisted the test subjects to drive closer to the designated path while achieving effectively the same speed. In the presence of HSNL, the drivers operated the tires in smaller slip angles and hence avoided saturation the front wheels’ lateral forces and excessive understeer. Finally, the support reduced their mental and physical demand. Part B of Chapter 7, studies the influence of HSNL during high speed cornering in a test-track. 17 test subjects drove around a narrow-twisting tarmac circuit, the aforementioned Opel Astra equipped with a variable steering feedback torque system. The drivers were instructed to achieve maximum velocity through corners, while receiving haptic steering feedback cues related to the vehicle’s cornering potentials. Driving aids (ABS and traction control) were disabled during testing. The test-track tests led to the conclusion that HSNL reduced drivers’ mental and physical demand. One of the primal goals of automotive manufacturers is to reduce the driver’s mental and control effort (c.f. Chapter 7); the work that will be presented in this thesis revealed that steering support near the vehicle’s handling limits can reduce the drivers’ mental and physical demand and can potentially promote safety. We can therefore conclude that certain of the developed support interfaces can be implemented into production vehicles.