Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22 Jan 201

Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics

The purpose of this study is to provide a detailed performance comparison of feature detector/descriptor methods, particularly when their various combinations are used for image-matching. The localization experiments of a mobile robot in an indoor environment are presented as a case study. In these experiments, 3090 query images and 127 dataset images were used. This study includes five methods for feature detectors (features from accelerated segment test (FAST), oriented FAST and rotated binary robust independent elementary features (BRIEF) (ORB), speeded-up robust features (SURF), scale invariant feature transform (SIFT), and binary robust invariant scalable keypoints (BRISK)) and five other methods for feature descriptors (BRIEF, BRISK, SIFT, SURF, and ORB). These methods were used in 23 different combinations and it was possible to obtain meaningful and consistent comparison results using the performance criteria defined in this study. All of these methods were used independently and separately from each other as either feature detector or descriptor. The performance analysis shows the discriminative power of various combinations of detector and descriptor methods. The analysis is completed using five parameters: (i) accuracy, (ii) time, (iii) angle difference between keypoints, (iv) number of correct matches, and (v) distance between correctly matched keypoints. In a range of 60{\deg}, covering five rotational pose points for our system, the FAST-SURF combination had the lowest distance and angle difference values and the highest number of matched keypoints. SIFT-SURF was the most accurate combination with a 98.41% correct classification rate. The fastest algorithm was ORB-BRIEF, with a total running time of 21,303.30 s to match 560 images captured during motion with 127 dataset images.Comment: 11 pages, 3 figures, 1 tabl

Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations

This paper introduces a unified mathematical definition for describing commonly used terms encountered in systematical analysis of automated driving systems in mixed-traffic simulations. The most significant contribution of this work is in translating the terms that are clarified previously in literature into a mathematical set and function based format. Our work can be seen as an incremental step towards further formalisation of Domain-Specific-Language (DSL) for scenario representation. We also extended the previous work in the literature to allow more complex scenarios by expanding the model-incompliant information using set-theory to represent the perception capacity of the road-user agents. With this dynamic perception definition, we also support interactive scenarios and are not limited to reactive and pre-defined agent behavior. Our main focus is to give a framework to represent realistic road-user behavior to be used in simulation or computational tool to examine interaction patterns in mixed-traffic conditions. We believe that, by formalising the verbose definitions and extending the previous work in DSL, we can support automatic scenario generation and dynamic/evolving agent behavior models for simulating mixed traffic situations and scenarios. In addition, we can obtain scenarios that are realistic but also can represent rare-conditions that are difficult to extract from field-tests and real driving data repositories

Simulation-based impact projection of autonomous vehicle deployment using real traffic flow

In this work we focus on future projected impacts of the autonomous vehicles in a realistic condition representing mixed traffic. By using real flow and speed data collected in 2002 and 2019 in the city of Gothenburg, we replicated and simulated the daily flow variation in SUMO. The expansion of the city in recent years was reflected in an increase in road users, and it is reasonable to expect it will increase further. Through simulations, it was possible to project this increase and to predict how this will impact the traffic in future. Furthermore, the composition of vehicle types in the future traffic can be expected to change through the introduction of autonomous vehicles. In order to predict the most likely drawbacks during the transition from a traffic consisting only manually driven vehicles to a traffic consisting only fully-autonomous vehicles, we focus on mixed traffic with different percentages of autonomous and manually driven vehicles. To realize this aim, several parameters of the car following and lane change models of autonomous vehicles are investigated in this paper. Along with the fundamental diagram, the number of lane changes and the number of conflicts are analyzed and studied as measures for improving road safety and efficiency

Potential impact of autonomous vehicles in mixed traffic from simulation using real traffic flow

This work focuses on the potential impacts of the autonomous vehicles in a mixed traffic condition represented in traffic simulator Simulation of Urban MObility (SUMO) with real traffic flow. Specifically, real traffic flow and speed data collected in 2002 and 2019 in Gothenburg were used to simulate daily flow variation in SUMO. In order to predict the most likely drawbacks during the transition from a traffic consisting only manually driven vehicles to a traffic consisting only fully-autonomous vehicles, this study focuses on mixed traffic with different percentages of autonomous and manually driven vehicles. To realize this aim, several parameters of the car following and lane change models of autonomous vehicles are investigated in this paper. Along with the fundamental diagram, the number of lane changes and the number of conflicts are analyzed and studied as measures for improving road safety and efficiency. The study highlights that the autonomous vehicles\u27 features that improve safety and efficiency in 100% autonomous and mixed traffic are different, and the ability of autonomous vehicles to switch between mixed and autonomous driving styles, and vice versa depending on the scenario, is necessary

Multi-sensor driver drowsiness monitoring

A system for driver drowsiness monitoring is proposed, using multi-sensor data acquisition and investigating two decision-making algorithms, namely a fuzzy inference system (FIS) and an artificial neural network (ANN), to predict the drowsiness level of the driver. Drowsiness indicator signals are selected allowing non-intrusive measurements. The experimental set-up of a driver-drowsiness-monitoring system is designed on the basis of the soughtafter indicator signals. These selected signals are the eye closure via pupil area measurement, gaze vector and head motion acquired by a monocular computer vision system, steering wheel angle, vehicle speed, and force applied to the steering wheel by the driver. It is believed that, by fusing these signals, driver drowsiness can be detected and drowsiness level can be predicted. For validation of this hypothesis, 30 subjects, in normal and sleep-deprived conditions, are involved in a standard highway simulation for 1.5 h, giving a data set of 30 pairs. For designing a feature space to be used in decision making, several metrics are derived using histograms and entropies of the signals. An FIS and an ANN are used for decision making on the drowsiness level. To construct the rule base of the FIS, two different methods are employed and compared in terms of performance: first, linguistic rules from experimental studies in literature and, second, mathematically extracted rules by fuzzy subtractive clustering. The drowsiness levels belonging to each session are determined by the participants before and after the experiment, and videos of their faces are assessed to obtain the ground truth output for training the systems. The FIS is able to predict correctly 98 per cent of determined drowsiness states (training set) and 89 per cent of previously unknown test set states, while the ANN has a correct classification rate of 90 per cent for the test data. No significant difference is observed between the FIS and the ANN; however, the FIS might be considered better since the rule base can be improved on the basis of new observations

Design of a Low-cost Tactile Robotic Sleeve for Autonomous Endoscopes and Catheters

Recent developments in medical robotics have been significant, supporting the minimally invasive operation requirements, such as smaller devices and more feedback available to surgeons. Nevertheless, the tactile feedback from a catheter or endoscopic type robotic device has been restricted mostly on the tip of the device and was not aimed to support the autonomous movement of the medical device during operation. In this work, we design a robotic sheath/sleeve with a novel and more comprehensive approach, which can function for whole-body or segment-based feedback control as well as diagnostic purposes. The robotic sleeve has several types of piezo-resistive pressure and extension sensors, which are embedded at several latitudes and depths of the silicone substrate. The sleeve takes the human skin as a biological model for its structure. It has a better tactile sensation of the inner tissues in the torturous narrow channels such as cardiovascular or endo-luminal tracts in human body thus can be used to diagnose abnormalities. In addition to this capability, using the stretch sensors distributed alongside its body, the robotic sheath/sleeve can perceive the ego-motion of the robotic backbone of the catheter and can act as a position feedback device. Because of the silicone substrate, the sleeve contributes toward safety of the medical device passively by providing a compliant interface. As an active safety measure, the robotic sheath can sense blood-clots or sudden turns inside a channel and by modifying the local trajectory, and can prevent embolisms or tissue rupture. In the future, advanced manufacturing techniques will increase the capabilities of the tactile robotic sleeve

An overview of novel actuators for soft robotics

In this systematic survey, an overview of non-conventional actuators particularly used in soft-robotics is presented. The review is performed by using well-defined performance criteria with a direction to identify the exemplary and potential applications. In addition to this, initial guidelines to compare the performance and applicability of these novel actuators are provided. The meta-analysis is restricted to five main types of actuators: shape memory alloys (SMAs), fluidic elastomer actuators (FEAs), shape morphing polymers (SMPs), dielectric electro-activated polymers (DEAPs), and magnetic/electro-magnetic actuators (E/MAs). In exploring and comparing the capabilities of these actuators, the focus was on eight different aspects: compliance, topology-geometry, scalability-complexity, energy efficiency, operation range, modality, controllability, and technological readiness level (TRL). The overview presented here provides a state-of-the-art summary of the advancements and can help researchers to select the most convenient soft actuators using the comprehensive comparison of the suggested quantitative and qualitative criteria

Parameter and density estimation from real-world traffic data: A kinetic compartmental approach

The main motivation of this work is to assess the validity of a LWR traffic flow model to model measurements obtained from trajectory data, and propose extensions of this model to improve it. A formulation for a discrete dynamical system is proposed aiming at reproducing the evolution in time of the density of vehicles along a road, as observed in the measurements. This system is formulated as a chemical reaction network where road cells are interpreted as compartments, the transfer of vehicles from one cell to the other is seen as a chemical reaction between adjacent compartment and the density of vehicles is seen as a concentration of reactant. Several degrees of flexibility on the parameters of this system, which basically consist of the reaction rates between the compartments, can be considered: a constant value or a function depending on time and/or space. Density measurements coming from trajectory data are then interpreted as observations of the states of this system at consecutive times. Optimal reaction rates for the system are then obtained by minimizing the discrepancy between the output of the system and the state measurements. This approach was tested both on simulated and real data, proved successful in recreating the complexity of traffic flows despite the assumptions on the flux-density relation