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In-vehicle signal processing plays an increasingly important role in driving behavior and traffic modeling. Maneuvers, influenced by the driver's choice and traffic/road conditions, are useful in understanding variations in driving performance and to help rebuild the intended route. Since different maneuvers are executed in varied lengths of time,...
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... the objective of maneuver correlation matrix is to have an indication of window lengths to be used in processing, it is possible to ignore those extremely low percentage cases so that to save computation cost. Table 3. Maneuver correlation matrix with tested probabilities in percentages The overall experimental procedure can be illustrated as Figure 4. Experiments use 60 driving sessions which include 3958 manually transcribed maneuver, and employ vehicle speed and steering wheel angle as the critical input signals. ...
Citations
... In our previous study, a similar HMM framework was employed in both a top-down as well as bottom-up approach to find the best integrated architecture for modeling driving behavior and recognizing maneuvers and routes [38]. Important features include steering wheel angle, speed, and brake signals from vehicle CAN bus data, or acceleration and gyroscope readings from a smart portable device [25], [39]. Recognition and prediction of lane-change maneuvers have been proposed together, suggesting a double-layered HMM framework in the consideration of both maneuver execution and route information [40]. ...
Vehicle technologies have advanced significantly over the past 20 years, especially with respect to novel in-vehicle systems for route navigation, information access, infotainment, and connected vehicle advancements for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity and communications. While there is great interest in migrating to fully automated, self-driving vehicles, factors such as technology performance, cost barriers, public safety, insurance issues, legal implications, and government regulations suggest it is more likely that the first step in the progression will be multifunctional vehicles. Today, embedded controllers as well as a variety of sensors and high-performance computing in present-day cars allow for a smooth transition from complete human control toward semisupervised or assisted control, then to fully automated vehicles. Next-generation vehicles will need to be more active in assessing driver awareness, vehicle capabilities, and traffic and environmental settings, plus how these factors come together to determine a collaborative safe and effective driver-vehicle engagement for vehicle operation. This article reviews a range of issues pertaining to driver modeling for the detection and assessment of distraction. Examples from the UTDrive project are used whenever possible, along with a comparison to existing research programs. The areas addressed include 1) understanding driver behavior and distraction, 2) maneuver recognition and distraction analysis, 3) glance behavior and visual tracking, and 4) mobile platform advancements for in-vehicle data collection and human-machine interface. This article highlights challenges in achieving effective modeling, detection, and assessment of driver distraction using both UTDrive instrumented vehicle data and naturalistic driving data
... Inspired by the possibility of applying non-uniform window frames [36], this study introduces a novel time-frequency spectral analysis method. Our previous studies [37] have proven that individual maneuvers have different and unique spectral characteristics of the steering angle signal, where the variations in the frequency content can provide information on maneuver boundaries. ...
In order to formulate a high-level understanding of driver behavior from massive naturalistic driving data, an effective approach is needed to automatically process or segregate data into low-level maneuvers. Besides traditional computer vision processing, this study addresses the lane-change detection problem by using vehicle dynamic signals (steering angle and vehicle speed) extracted from the CAN-bus, which is collected with 58 drivers around Dallas, TX area. After reviewing the literature, this study proposes a machine learning based segmentation and classification algorithm, which is stratified into three stages. The first stage is pre-processing and pre-filtering, which is intended to reduce noise and remove clear left and right turning events. Second, a spectral time-frequency analysis segmentation approach is employed to generalize all potential time-variant lane-change and lane-keeping candidates. The final stage compares two possible classification methods – (a) Dynamic Time Warping (DTW) feature with k-Nearest Neighbor (k-NN) classifier; and (b) hidden state sequence prediction with a combined Hidden Markov Model (HMM). The overall optimal classification accuracy can be obtained at 80.36% for lane-change-left and 83.22% for lane-change-right. The effectiveness and issues of failures are also discussed. With the availability of future large-scale naturalistic driving data such as SHRP2, this proposed effective lane-change detection approach can further contribute to characterize both automatic route recognition as well as distracted driving state analysis.
Accurate trajectory prediction of surrounding vehicles is important for automated vehicles. To solve several existing problems of maneuver-based trajectory prediction, we propose four targeted solutions and establish a trajectory prediction model that integrates semi-supervised And-or Graph (AOG) and Spatio-temporal LSTM (ST-LSTM). To reduce the dependence on the well-labeled dataset, we introduce the concept of sub-maneuvers to improve the classifications of vehicle movements based on the given rough maneuver labels. AOG is used as the backbone of the probabilistic motion inference considering sub-maneuvers. We only define the basic units and inference logics of AOG and design a semi-supervised approach to directly learn the sub-maneuvers and the inference model structure from the training data, without manually specifying the structure (layers and nodes) of the inference model. This approach helps to avoid excessive artificial design or biases. The learned hierarchical motion inference model improves the interpretability of the overall trajectory prediction process. To utilize vehicle interaction information and further yield more accurate prediction, we adopt two different methods to consider vehicle interaction in the two sub-models (maneuver recognition and trajectory prediction). The experiment on NGSIM I-80 dataset shows that the maneuver-based model proposed in this paper (AOG-ST and refined AOG-ST-TB) performs more accurate trajectory prediction results. Although the AOG-ST seems clumsy and slow, we show that it is a flexible and quick model for trajectory prediction for various driving scenarios through the discussion and experiment.
It is a widely accepted view that considering the memory effects of historical information (driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However, many commonly used models (e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range (ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM (ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.
