James R. Ward

Vehicle collision probability calculation for general traffic scenarios under uncertainty

Vehicle-to-vehicle (V2V) communication systems allow vehicles to share state information with one another to improve safety and efficiency of transportation networks. One of the key applications of such a system is in the prediction and avoidance of collisions between vehicles. If a method to do this is to succeed it must be robust to measurement uncertainty. The method should also be general enough that it does not rely on constraints on vehicle motion for the accuracy of its predictions. It should work for all interactions between vehicles and not just a select subset. This paper presents a method for collision probability calculation that addresses these problems.

An Unsupervised Approach for Inferring Driver Behavior From Naturalistic Driving Data

Intelligent transportation systems are able to collect large volumes of high-resolution data. The amount of data collected by these systems can quickly overwhelm the ability of human analysts to draw meaningful conclusions from the data, particularly in large-scale multivehicle field trials. As advanced driver assistance systems develop, they will also be required to form a rich and high-level understanding of the world from the data they receive, including the behavior of the driver. These applications motivate the need for unsupervised tools capable of forming a high-level summary of low-level driving data. This paper presents an unsupervised method for converting naturalistic driving data into high-level behaviors. The proposed method works in two steps. In the first step, inertial data are automatically decomposed into linear segments. In the second step, the segments are assigned to high-level driving behaviors. The proposed method is computationally efficient and completely unsupervised and requires minimal preprocessing. Although the method is unsupervised, the clusters produced exhibit high-level patterns that can easily be associated with driving behaviors such as braking, turning, accelerating, and coasting. The effectiveness of the proposed algorithms is demonstrated in an offline application where the objective is to summarize inertial data into driving behaviors. The method is also demonstrated in an online application where the aim is to infer the current driving behavior using only inertial data. Both experiments were conducted using driving data collected in natural driving conditions.

The Warrigal Dataset: Multi-Vehicle Trajectories and V2V Communications

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. Development of such systems must be based upon data derived from actual interactions if they are to be effective when used in real world applications. Increasingly, systems are being developed that are based on radio communication of state and intent between vehicles. Understanding of how these interactions occur is also necessary to creating robust systems. In order to test and compare new techniques, approaches and algorithms it is necessary to have a rich dataset to experiment with. This paper presents a detailed dataset useful for members of the Intelligent Transportation Systems community. It contains vehicle state information, vehicle-to-vehicle communications and road maps at high temporal resolution for large numbers of interacting vehicles over a long time period. This data set has already been used for a number of Intelligent Transportation Systems projects such as road mapping, driver intent prediction and collision avoidance among others.

Predicting Driver Intent from Models of Naturalistic Driving

Modern advanced driver assistance systems (ADAS) have lead to safer vehicles. However, current ADAS are typically limited to a reactive, physical model of the vehicle. They lack the ability to understand complex traffic scenarios. One traffic scenario that has gathered interest in recent years is the problem of inferring driver behaviour at road features such as intersections. At these locations drivers may choose to perform one of many available manoeuvres. Early identification of the manoeuvre is important for the development of future safety and situational awareness systems. The objective of this paper is to develop a method for predicting which manoeuvre a driver will execute. To fulfil this objective a simple method based on quadratic discriminant analysis is proposed. The method is computationally efficient and developed with a view to being applied to complex road networks using naturalistic driving data. The proposed method is demonstrated and validated using naturalistic driving data collected at a three way T-intersection.

Fault Detection for Vehicular Ad Hoc Wireless Networks

Abstract-An increasing number of intelligent transportation applications require robust and reliable wireless ad hoc communication. The process of communicating using radio requires a series of software and hardware modules to be functioning correctly. For many vehicle safety and automation applications communication is relied upon to the point where undetected faults can result in potentially dangerous situations, for example if a warning cannot be given in time to prevent a collision. The consequence of problems with any of the network components can be a partial or complete loss of radio communication. Generally, most systems will consider network failure when there is no communication, but this overlooks problems where a partial fault causes degradation in the communication performance. There is a fundamental requirement to detect and respond to the partial failure of a network to ensure that communication is not intermittent, or performs poorly after a certain range. The partial loss of communication is difficult to detect, and is often overlooked in mobile ad hoc network applications. This paper introduces a novel method for modeling the antenna performance using collected data, and using the model to determine the probability that an antenna has some level of performance degradation.

Automated Extraction of Driver Behaviour Primitives Using Bayesian Agglomerative Sequence Segmentation

The low-level building blocks of driver behaviour have been shown to exhibit statistical patterns such as periods of turning, braking and acceleration, as well as different combinations of these. Collectively, these patterns can be regarded as a language of “driving primitives.” This allows us to reason about more meaningful driving maneuvers, e.g. overtaking, parking, by treating them as sequences of primitives. In this paper we introduce a method for automatically finding the boundaries between primitives, which is important when analysing large volumes of raw sensor data that can be generated in ITS applications. Our method is cost-effective, completely unsupervised and requires minimal preprocessing. We demonstrate the potential of our approach via an experiment with genuine data from an inertial measurement unit.

GPS/GNSS Consistency in a Multi-path Environment and During Signal Outages

The majority of Intelligent Transportation System (ITS) applications require an estimate of position, often generated through the fusion of satellite based positioning (such as GPS) with on-board inertial systems. To make the position estimates consistent it is necessary to understand the noise distribution of the information used in the estimation algorithm. For GNSS position information the noise distribution is commonly approximated as zero mean with Gaussian distribution, with the standard deviation used as an algorithm tuning parameter. A major issue with satellite based positioning is the well known problem of multipath which can introduce a non-linear and non-Gaussian error distribution for the position estimate. This paper introduces a novel algorithm that compares the noise distribution of the GNSS information with the more consistent noise distribution of the local egocentric sensors to effectively reject GNSS data that is inconsistent. The results presented in this paper show how the gating of the GNSS information in a strong multipath environment can maintain consistency in the position filter and dramatically improve the position estimate. This is particularly important when sharing information from different vehicles as in the case of cooperative perception due to the requirement to align information from various sources.

Comprehensive data collection and context based metric evaluation for safety monitoring

The only direct method to evaluate safety is to monitor the number of accidents and near misses. However, vehicle accidents are statistically infrequent and near misses are heavily underreported, making this approach unfeasible. An alternative strategy is to examine and evaluate metrics which have been shown to be precursors to accidents. The widespread use of metric evaluation for measuring safety in vehicle operations is limited due to a lack of ubiquitous data collection and communication systems. In addition, the effective evaluation of a safety metric is strongly dependent on the high-level context of the situation. This paper presents a system that records and exchanges data and context information to facilitate the calculation of informative safety metrics, and shows results from a number of implementations of this system in a mining context.

Fault detection for vehicular ad-hoc wireless networks

An increasing number of intelligent transportation applications require robust and reliable wireless communication. To achieve the required quality of service it is necessary to implement redundancy in the critical path which includes the radio software and hardware. In a real-world application there are many things that can cause the communication between two vehicles to degrade or stop completely. This paper describes a novel technique for detecting degradation or failure of communication links by comparing the performance of the radios to a probabilistic model built using data collected in the field. The results show that this techinique can successfully detect when there is partial or complete failure to communicate due to damage to the external components such as antennas, connectors and cables.

A Flexible System Architecture for Acquisition and Storage of Naturalistic Driving Data

Innovation in intelligent transportation systems relies on analysis of high-quality data. In this paper, we describe the design principles behind our data management infrastructure. The principles we adopt place an emphasis on flexibility and maintainability. This is achieved by breaking up code into a modular design that can be run on many independent processes. Message passing over a publish-subscribe network enables interprocess communication and promotes data-driven execution. By following these principles, rapid prototyping and experimentation with new sensing modalities and algorithms are possible. The communication library underpinning our proposed architecture is compared against several popular communication libraries. Features designed into the system make it decentralized, robust to failure, and amenable to scaling across multiple machines with minimal configuration. Code written using the proposed architecture is compact, transparent, and easy to maintain. Experimentation shows that our proposed architecture offers a high performance when compared against alternative communication libraries.