Driver behavior classification and lateral control for automobile safety systems

Advanced driver assistance systems (ADAS) have been developed to help drivers maintain stability, improve road safety, and avoid potential collision. The data acquisition equipment that can be used to measure the state and parameter information of the vehicle may not be available for a standard passenger car due to economical and technical limitations. This work focuses on developing three technologies (longitudinal tire force estimation, driver behavior classification and lateral control) using low-cost sensors that can be utilized in ADAS. For the longitudinal tire force estimation, a low cost 1Hz positioning global system (GPS) and a steering angle sensor are used as the vehicle data acquisition equipment. A nonlinear extended two-wheel vehicle dynamic model is employed. The sideslip angle and the yaw rate are estimated by discrete Kalman Filter. A time independent piecewise optimization scheme is proposed to provide time-continuous estimates of longitude tire force, which can be transferred to the throttle/brake pedal position. The proposed method can be validated by the estimation results. Driver behavior classification systems can detect unsafe driver behavior and avoid potentially dangerous situations. To realize this strategy, a machine learning classification method, Gaussian Mixture model (GMM), is applied to classify driver behavior. In this application, a low cost 1Hz GPS receiver is considered as the vehicle data acquisition equipment instead of other more costly sensors (such as steering angle sensor, throttle/brake position sensor, and etc.). Since the driving information is limited, the nonlinear extended two-wheel vehicle dynamic model is adopted to reconstruct the driver behavior. Firstly, the sideslip angle and the yaw rate are calculated since they are not available from the GPS measurements. Secondly, a piecewise optimization scheme is proposed to reproduce the steering angle and the longitudinal force. Finally, a GMM classifier is trained to identify abnormal driver behavior. The simulation results demonstrated that the proposed scenario can detect the unsafe driver behavior effectively. The lateral control system developed in this study is a look-down reference system which uses a magnetic sensor at the front bumper to measure the front lateral displacement and a GPS to measure the vehicle\u27s heading orientation. Firstly, the steering angles can be estimated by using the data provided by the front magnetic sensor and GPS. The estimation algorithm is an observer for a new extended single-track model, in which the steering angle and its derivative are viewed as two state variables. Secondly, the road curvature is determined based on the linear relationship with respect to the steering angle. Thirdly, an accurate and real-time estimation of the vehicle\u27s lateral displacements can be accomplished according to a state observer. Finally, the closed loop controller is used as a compensator for automated steering. The proposed estimation and control algorithms are validated by simulation results. The results showed that this lateral steering control system achieved a good and robust performance for vehicles following or tracking a reference path

Lateral string stability of vehicle platoons

This internship report is part of a larger assignment which is an analysis of lateral string stability and the development of a controller design method with guaranteed lateral string stability. Lateral string stability is an issue when a look-ahead sensing method is used in combination with a vehicle-following control strategy. The coupling between vehicles enables errors to increase while they propagate upstream through a string of vehicles. Communicating desired yaw rate or lateral acceleration and use this information for controller design could be an option to achieved guaranteed lateral string stability. One of the applications of lateral control will be conducting maneuvers like merging or lane changes. During these maneuvers the side-slip angles of the tyres stay within theinterval of linear tyre response, this means that side-slip angles of the tyres are within 0:5. On this interval the non-linear and linearized tyre model have the same linear response. This makes is possible to use a linearized vehicle model with linear tyres for the modeling of the lateral and yaw dynamics of the vehicle. This is validated using experimental data and it is shown that the response of the linearized vehicle model is almost equal to the actual vehicle response

Algebraic nonlinear estimation and flatness-based lateral/longitudinal control for automotive vehicles

6 pagesInternational audienceA combined longitudinal and lateral vehicle control is presented. It employs flatness-based control and new algebraic estimation techniques for the numerical differentiation of noisy signals. This nonlinear control is designed for automatic path-tracking via vehicle steering angle and driving/braking wheel torque. It combines the control of the lateral and longitudinal movements in order to ensure an accurate tracking of straight or curved trajectories. It can also be used to perform a combined lane-keeping and steering control during critical driving situations such as obstacle avoidance, stop-and-go control, lane-change maneuvers or any other maneuvers. Promising results have been obtained using the noisy experimental data acquired by a laboratory vehicle under high dynamic loads and characterized by high lateral accelerations

Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

Path Tracking Control for Autonomous Driving Applications

Autonomous or self-driving vehicles are becoming a consolidate reality that involves both industrial and academic elds also for its impact in social and governmental communities, well far from automotive engineering. The intent of the present paper is to design an automatic steering control for an autonomous vehicle equipped with steer-by-wire and drive-by-wire technologies. The steering action is calculated to let the vehicle follow a reference path which is stored in a Digital Map properly built to be available in real-time. A Proportional + Derivative (PD) control strategy is deigned based on the Parameter State Approach (PSA) and it is coupled with a Feedforward (FF) term for improving the path tracking control in cornering maneuvers. Some experimental results are shown to demonstrates the ecacy of the controller presented

Back-stepping variable structure controller design for off-road intelligent vehicle

In this paper, off-road path recognition and navigation control method are studied to realize intelligent vehicle autonomous driving in unstructured environment. Firstly, the traversable path is achieved by vision and laser sensors. The vehicle steering and driving coupled dynamic model is established. Secondly, a coordinated controller for steering and driving is proposed via the back-stepping variable structure control method, which can be used to deal with the unmatched uncertainties of the control system model. To reduce the chattering phenomenon caused by variable structure, the boundary layer approach is introduced. The results of simulation and off-road experiment show the effectiveness and robustness of the proposed controller