Data-driven situation awareness algorithm for vehicle lane change

A good level of situation awareness is critical for vehicle lane change decision making. In this paper, a Data-Driven Situation Awareness (DDSA) algorithm is proposed for vehicle environment perception and projection using machine learning algorithms in conjunction with the concept of multiple models. Firstly, unsupervised learning (i.e., Fuzzy C-Mean Clustering (FCM)) is drawn to categorize the drivers’ states into different clusters using three key features (i.e., velocity, relative velocity and distance) extracted from Intelligent Driver Model (IDM). Statistical analysis is conducted on each cluster to derive the acceleration distribution, resulting in different driving models. Secondly, supervised learning classification technique (i.e., Fuzzy k-NN) is applied to obtain the model/cluster of a given driving scenario. Using the derived model with the associated acceleration distribution, Kalman filter/prediction is applied to obtain vehicle states and their projection. The publicly available NGSIM dataset is used to validate the proposed DDSA algorithm. Experimental results show that the proposed DDSA algorithm obtains better filtering and projection accuracy in comparison with the Kalman filter without clustering

A quantitative approach to behavioural analysis of drivers in highways using particle filtering

The analysis of the driving behaviour is a challenging area in transport that has applications in numerous fields ranging from highway design to micro-simulation and development of advanced driver assistance systems (ADAS). There has been evidence suggesting changes in the driving behaviour in response to changes in traffic conditions, and this is known as adaptive driving behaviour. Identifying these changes, the conditions under which they happen, and describing them in a systematic way would contribute greatly to the accuracy of micro-simulation and more importantly to the understanding of the traffic flow, and will therefore pave the way for introducing further improvements in the efficiency of the transport network. In this paper adaptive driving behaviour is linked to changes in the model parameters for a given car-following model. These changes are tracked using a dynamic system identification method, namely unscented particle filtering

A system for traffic violation detection

This paper describes the framework and components of an experimental platform for an advanced driver assistance system (ADAS) aimed at providing drivers with a feedback about traffic violations they have committed during their driving. The system is able to detect some specific traffic violations, record data associated to these faults in a local data-base, and also allow visualization of the spatial and temporal information of these traffic violations in a geographical map using the standard Google Earth tool. The test-bed is mainly composed of two parts: a computer vision subsystem for traffic sign detection and recognition which operates during both day and nighttime, and an event data recorder (EDR) for recording data related to some specific traffic violations. The paper covers firstly the description of the hardware architecture and then presents the policies used for handling traffic violations

Real-time smoothing of car-following data through sensor-fusion techniques

AbstractObservation of vehicles kinematics is an important task for many applications in ITS (Intelligent Transportation Systems). It is at the base of both theoretical analyses and application developments, especially in case of positioning and tracing/tracking of vehicles, car-following analyses and models, navigation and other ATIS (Advanced Traveller Information Systems), ACC (Adaptive Cruise Control) systems, CAS and CWS (Collision Avoidance Systems and Collision Warning Systems) and other ADAS (Advanced Driving Assistance Systems). Modern technologies supply low-cost devices able to collect time series of kinematic and positioning data with medium to very high frequency. Even more data can be (almost continually) collected if vehicle-to-vehicle (V2V) communications come true. However, some of the ITS applications (as well as car-following models, on which many ADAS and ACC are based) require highly accurate measures or, at least, smooth profiles of collected data. Unfortunately, even relatively high-cost devices can collect biased data because of many technical reasons and often this bias could lead to unrealistic kinematics, incorrect absolute positioning and/or inconsistencies between vehicles (e.g. negative spacing). As a consequence, data need filtering in most of the ITS applications. To this aim proper algorithms are required and several sensors and sources of data possibly integrated in order to obtain the maximum quality at the minimal cost. This work addresses the previous issues by developing a specific Kalman smoothing approach. The approach is developed in order to deal with car-following conditions but is conceived to take into account also navigation issues. The performances are analysed with respect to real-world car-following data, voluntarily biased for evaluation purposes. Assessment is carried out with reference to different mixtures of sensors and different sensors accuracies

Data-driven situation awareness algorithm for vehicle lane change

A good level of situation awareness is critical for vehicle lane change decision making. In this paper, a Data-Driven Situation Awareness (DDSA) algorithm is proposed for vehicle environment perception and projection using machine learning algorithms in conjunction with the concept of multiple models. Firstly, unsupervised learning (i.e., Fuzzy C-Mean Clustering (FCM)) is drawn to categorize the drivers’ states into different clusters using three key features (i.e., velocity, relative velocity and distance) extracted from Intelligent Driver Model (IDM). Statistical analysis is conducted on each cluster to derive the acceleration distribution, resulting in different driving models. Secondly, supervised learning classification technique (i.e., Fuzzy k-NN) is applied to obtain the model/cluster of a given driving scenario. Using the derived model with the associated acceleration distribution, Kalman filter/prediction is applied to obtain vehicle states and their projection. The publicly available NGSIM dataset is used to validate the proposed DDSA algorithm. Experimental results show that the proposed DDSA algorithm obtains better filtering and projection accuracy in comparison with the Kalman filter without clustering

An Overview on Study of Identification of Driver Behavior Characteristics for Automotive Control

Driver characteristics have been the research focus for automotive control. Study on identification of driver characteristics is provided in this paper in terms of its relevant research directions and key technologies involved. This paper discusses the driver characteristics based on driver’s operation behavior, or the driver behavior characteristics. Following the presentation of the fundamental of the driver behavior characteristics, the key technologies of the driver behavior characteristics are reviewed in detail, including classification and identification methods of the driver behavior characteristics, experimental design and data acquisition, and model adaptation. Moreover, this paper discusses applications of the identification of the driver behavior characteristics which has been applied to the intelligent driver advisory system, the driver safety warning system, and the vehicle dynamics control system. At last, some ideas about the future work are concluded

Ergonomics of intelligent vehicle braking systems

The present thesis examines the quantitative characteristics of driver braking and pedal operation and discusses the implications for the design of braking support systems for vehicles. After the current status of the relevant research is presented through a literature review, three different methods are employed to examine driver braking microscopically, supplemented by a fourth method challenging the potential to apply the results in an adaptive brake assist system. First, thirty drivers drove an instrumented vehicle for a day each. Pedal inputs were constantly monitored through force, position sensors and a video camera. Results suggested a range of normal braking inputs in terms of brake-pedal force, initial brake-pedal displacement and throttle-release (throttle-off) rate. The inter-personal and intra-personal variability on the main variables was also prominent. [Continues.