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

Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model

Background Road collisions and casualties pose a serious threat to commuters around the globe. Autonomous Vehicles (AVs) aim to make the use of technology to reduce the road accidents. However, the most of research work in the context of collision avoidance has been performed to address, separately, the rear end, front end and lateral collisions in less congested and with high inter-vehicular distances. Purpose The goal of this paper is to introduce the concept of a social agent, which interact with other AVs in social manners like humans are social having the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. The proposed social agent is based on a human-brain inspired mentalizing and mirroring capabilities and has been modelled for collision detection and avoidance under congested urban road traffic. Method We designed our social agent having the capabilities of mentalizing and mirroring and for this purpose we utilized Exploratory Agent Based Modeling (EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by Niazi and Hussain. Results Our simulation and practical experiments reveal that by embedding Richardson's arms race model within AVs, collisions can be avoided while travelling on congested urban roads in a flock like topologies. The performance of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure

Event-Triggered Multi-Lane Fusion Control for 2-D Vehicle Platoon Systems with Distance Constraints

This paper investigates the event-triggered fixedtime multi-lane fusion control for vehicle platoon systems with distance keeping constraints where the vehicles are spread in multiple lanes. To realize the fusion of vehicles in different lanes, the vehicle platoon systems are firstly constructed with respect to a two-dimensional (2-D) plane. In case of the collision and loss of effective communication, the distance constraints for each vehicle are guaranteed by a barrier function-based control strategy. In contrast to the existing results regarding the command filter techniques, the proposed distance keeping controller can constrain the distance tracking error directly and the error generated by the command filter is coped with by adaptive fuzzy control technique. Moreover, to offset the impacts of the unknown system dynamics and the external disturbances, an unknown input reconstruction method with asymptotic convergence is developed by utilizing the interval observer technique. Finally, two relative threshold triggering mechanisms are utilized in the proposed fixed-time multi-lane fusion controller design so as to reduce the communication burden. The corresponding simulation results also verify the effectiveness of the proposed strategy

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

Robustness analysis of evolutionary controller tuning using real systems

A genetic algorithm (GA) presents an excellent method for controller parameter tuning. In our work, we evolved the heading as well as the altitude controller for a small lightweight helicopter. We use the real flying robot to evaluate the GA's individuals rather than an artificially consistent simulator. By doing so we avoid the ldquoreality gaprdquo, taking the controller from the simulator to the real world. In this paper we analyze the evolutionary aspects of this technique and discuss the issues that need to be considered for it to perform well and result in robust controllers

Cooperative Throttle and Brake Fuzzy Control for ACC+Stop&Go Maneuvers

The authors are with the Industrial Computer Science Department, Instituto de Automática Industrial (CSIC), 28500 Madrid, SpainThe goal that a car be driven autonomously is far in the future and probably unreachable, but as a first step in that direction, adaptive cruise control (ACC) and Stop&Go maneuver systems are being developed. These kind of controllers adapt the speed of a car to that of the preceding one (ACC) and get the car to stop if the lead car stops. This paper presents one such system and related experiments performed on a real road with real cars. The driving system gets its input via an RTK DGPS device and communicates its positions to one another via a wireless local area network link. It outputs signals controlling the pressure on the throttle and brake pedals. The control system is based on fuzzy logic, which is considered best to deal with processes as complex as driving. Two mass produced Citroën Berlingo electric vans have been instrumented, providing them with computer controlled actuators over the brake and the throttle to achieve human-like driving. The results of the experiments show that the behavior of the vehicles is very close to human and that they adapt to driving incidences, increasing the safety of the driving and permitting cooperation with manually driven cars.This work was supported in part by the Spanish Ministry of Education under Grant ISAAC CICYT DPI2002-04064-C05-02, by the Spanish Ministry of Public Works under Grant COPOS BOE 280 22/11/2002, and by the Res. 22778, Citroën España S.A. under Contract “Adquirir nuevos conocimientos sobre la introducción de las tecnologías de la información en el mundo del automóvil y para difundirlos en los ámbitos científicos, empresariales y comerciales (AUTOPIA),” and by Cybecars-2 Project UE-STREP 28062, 6th Framework Programme, 2006.Peer reviewe