Edward Holweg

Bio: Edward Holweg is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Vehicle dynamics & Control theory. The author has an hindex of 12, co-authored 23 publications receiving 426 citations.

Hybrid ABS Control Using Force Measurement

Abstract: The anti-lock braking system (ABS) is the most important active safety system for passenger cars. Thanks to tire force measurement, provided for example by the new SKF load sensing hub bearing units, hybrid ABS algorithms can be made simpler and more robust than when only using wheel acceleration measurement. A two-phase algorithm is presented, where the wheel acceleration is controlled in closed-loop and the longitudinal force measurement is used to fire phase switching. Load transfer is accounted for using the vertical force measurement. Realistic simulations show that this simple algorithm can handle changes in vehicle velocity and tire-road friction without extra logic or adaptation of the controller parameters. Stability analysis provides tuning indications. Finally, the algorithm is validated on a tire-in-the-loop experimental facility.

Steering Force Feedback for Human–Machine-Interface Automotive Experiments

Abstract: Driving-simulator fidelity is usually defined by the quality of its visual and motion cueing system. However, the quality of its haptic cues is also very important and is determined by both hardware and control properties. Most experiments with haptic steering systems employ commercially available systems and do not address the system's fidelity. The goal of this paper is to offer guidelines for the development of hardware, performance evaluation, and system control in order to engineer realistic haptic cues on the steering wheel. A relatively low-cost solution for hardware is deployed, consisting of a velocity-controlled three-phase brushless servomotor, of which its high-bandwidth control allows for a realistic representation of forces. A method is presented to overcome electromagnetic interference produced by the industrial servomotor and the controller through careful amplification and filtering. To test the system, different inertia-spring-damper systems were simulated and evaluated in time and frequency domain. In conclusion, the designed system allowed reproduction of a large range of steering-wheel dynamics and forces. As a result, the developed system constitutes an efficient haptic device for human-machine-interface automotive experiments.

Driver's Arms' Time-Variant Neuromuscular Admittance During Real Car Test-Track Driving

Abstract: Attempts to measure and model driver steering behavior have been so far mainly performed with driving simulators and time-invariant techniques. The goal of this paper was to quantify the driver's arms' time-variant admittance in real driving and to provide a range of parametrically fitted values on the estimated frequency response functions. The human arms' neuromuscular (NMS) admittance was estimated by applying torque disturbances on the steering wheel during real car test-track driving. To capture the time-variant behavior, the admittance was estimated using a 1.28-s sliding time window. The results showed that drivers adapt their admittance while cornering, exposing a variant behavior during different corners and driving speeds. The frequency response function (FRF) of the admittance while cornering has the properties of a second-order system. During cornering, drivers have increased stiffness values, whilst in straight driving, the FRFs resemble a second-order system ( -40 dB/decade gain drop; double pole at low frequencies) for low frequencies, with a zero for frequencies above 6 Hz (on average). The FRFs during cornering were parametrically fitted to a second-order inertia-spring-damper model. The fitted parameter values can be used for NMS driver models and motivate the stability analysis of the combined closed-loop driver steering system.

Race-Car Instrumentation for Driving Behavior Studies

Abstract: This paper supplies a roadmap on how a researcher can effectively perform real vehicular experiments oriented to high-speed driving research. It provides detailed guidelines for constructing versatile low-cost instrumentation suitable to be fitted on race cars. The custom-built equipment, consisting of wheel-speed sensors, steering angle-torque sensors, electronic boards, etc., is thoroughly described. Furthermore, this paper depicts the required processing from raw measurements to user-friendly data suitable for driver behavior studies. As an illustration, a case study on driving behavior analysis is presented, during the execution of high-speed circular maneuvers. The recorded data showed markedly different driving behaviors between expert and novice drivers. The mechanical designs and the open-source-based software are freely available online.

Estimating driving behavior by a smartphone

Abstract: In this paper, we propose an approach to understand the driver behavior using smartphone sensors. The aim for analyzing the sensory data acquired using a smartphone is to design a car-independent system which does not need vehicle mounted sensors measuring turn rates, gas consumption or tire pressure. The sensory data utilized in this paper includes the accelerometer, gyroscope and the magnetometer. Using these sensors we obtain position, speed, acceleration, deceleration and deflection angle sensory information and estimate commuting safety by statistically analyzing driver behavior. In contrast to state of the art, this work uses no external sensors, resulting in a cost efficient, simplistic and user-friendly system.

In the Passenger Seat: Investigating Ride Comfort Measures in Autonomous Cars

Abstract: The prospect of driverless cars wide-scale deployment is imminent owing to the advances in robotics, computational power, communications, and sensor technologies. This promises highway fatality reductions and improvements in traffic and fuel efficiency. Our understanding of the effects arising from commuting in autonomous cars is still limited. The novel concept of the loss of driver controllability is introduced here. It requires a reassessment of vehicle's comfort criteria. In this review paper, traditional comfort measures are examined and autonomous passenger awareness factors are proposed. We categorize path-planning methods in light of the offered factors. The objective of the review presented in this article is to highlight the gap in path planning from a passenger comfort perspective and propose some research solutions. It is expected that this investigation will generate more research interest and bring innovative solutions into this field.

Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach

Abstract: This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.

Closed-loop subspace identification methods: an overview

Abstract: In this study, the authors present an overview of closed-loop subspace identification methods found in the recent literature Since a significant number of algorithms has appeared over the last decade, the authors highlight some of the key algorithms that can be shown to have a common origin in autoregressive modelling Many of the algorithms found in the literature are variants on the algorithms that are discussed here In this study, the aim is to give a clear overview of some of the more successful methods presented throughout the last decade Furthermore, the authors retrace these methods to a common origin and show how they differ The methods are compared both on the basis of simulation examples and real data Although the main focus in the literature has been on the identification of discrete-time models, identification of continuous-time models is also of practical interest Hence, the authors also provide an overview of the continuous-time formulation of the identification framework

A Review of Shared Control for Automated Vehicles: Theory and Applications

Abstract: The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.