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

Personalized driver workload inference by learning from vehicle related measurements

Adapting in-vehicle systems (e.g. Advanced Driver Assistance Systems, In-Vehicle Information Systems) to individual drivers’ workload can enhance safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in a proper manner. Rather than developing an average model for all drivers, a Personalized Driver Workload Inference (PDWI) system considering individual drivers’ driving characteristics is developed using machine learning techniques via easily accessed Vehicle Related Measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual drivers’ workload is first automatically splitted into different categories according to its inherent data characteristics using Fuzzy C means clustering. Then an implicit mapping between VRMs and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified into different clusters, consequently driver workload can be successfully inferred. A recently collected dataset from real-world naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating Fuzzy C-means clustering and Support Vector Machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F1-score and prediction time. The inter-individual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness

Dimension reduction aided hyperspectral image classification with a small-sized training dataset: experimental comparisons

Hyperspectral images (HSI) provide rich information which may not be captured by other sensing technologies and therefore gradually find a wide range of applications. However, they also generate a large amount of irrelevant or redundant data for a specific task. This causes a number of issues including significantly increased computation time, complexity and scale of prediction models mapping the data to semantics (e.g., classification), and the need of a large amount of labelled data for training. Particularly, it is generally difficult and expensive for experts to acquire sufficient training samples in many applications. This paper addresses these issues by exploring a number of classical dimension reduction algorithms in machine learning communities for HSI classification. To reduce the size of training dataset, feature selection (e.g., mutual information, minimal redundancy maximal relevance) and feature extraction (e.g., Principal Component Analysis (PCA), Kernel PCA) are adopted to augment a baseline classification method, Support Vector Machine (SVM). The proposed algorithms are evaluated using a real HSI dataset. It is shown that PCA yields the most promising performance in reducing the number of features or spectral bands. It is observed that while significantly reducing the computational complexity, the proposed method can achieve better classification results over the classic SVM on a small training dataset, which makes it suitable for real-time applications or when only limited training data are available. Furthermore, it can also achieve performances similar to the classic SVM on large datasets but with much less computing time

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

HEER - a delay-aware and energy-efficient routing protocol for wireless sensor networks

Minimizing energy consumption to maximize network lifetime is one of the crucial concerns in designing wireless sensor network routing protocols. Cluster-based protocols have shown promising energy-efficiency performance, where sensor nodes take turns to act as cluster heads (CHs), which carry out higher-level data routing and relaying. In such case the energy consumption is more evenly distributed for all the nodes. However, most cluster-based protocols improve energy-efficiency at the cost of transmission delay. In this paper, we propose an improved delay-aware and energy-efficient clustered protocol called Hamilton Energy-Efficient Routing Protocol (HEER). HEER forms clusters in the network initialization phase and links members in each cluster on a Hamilton Path, constructed using a greedy algorithm, for data transmission purpose. No cluster reformation is required and the members on the path will take turns to become cluster head. The design allows HEER to save on network administration energy and also balance the load comparing to traditional cluster-based protocols. The algorithms designed in HEER also means that it does not suffer long delay and does not require each node to have global location information comparing with classic chain-based protocols such as PEGASIS and its variations. We implemented the HEER protocol in MATLAB simulation and compared it with several cluster-based and chain-based protocols. We found that HEER is able to achieve an improved network lifetime over the current protocols while maintaining the average data transmission delay. In the simulation, HEER achieved 66.5% and 40.6% more rounds than LEACH and LEACH-EE, which are cluster-based protocols. When compared with chain-based protocols (PEGASIS and Intra-grid-PEGSIS), HEER managed 21.2 times and 16.7 times more rounds than PEGASIS and Intra-grid-PEGASIS respectively. In addition, HEER can eliminated 90% of transmission delay comparing to LEACH and LEACH-EE and 99% comparing with PEGASIS and Intra-grid-PEGASIS

Probabilistic faster R-CNN with stochastic region proposing: Towards object detection and recognition in remote sensing imagery

Object detection is one of the most important tasks involved in intelligent agriculture systems, especially in pest detection. This paper focuses on a most devastated agricultural disaster: grasshopper plagues. Grasshopper detection and monitoring is of paramount importance in preventing grasshopper plagues. This paper proposes a probabilistic faster R-CNN algorithm with stochastic region proposing, where a probabilistic region proposal network, an image classification network, and an object detection network are integrated to detect and locate grasshoppers. More specifically, in the proposed framework, the probabilistic region proposal network considers attributes (e.g. size, shape) of region proposals and the image classification network identifies the existence of grasshoppers while the object detection network scores recognition confidence for a region proposal. By integrating these three networks, the uncertainty can be passed from end to end, and the final confidence is obtained for each region proposal can be explicitly quantified. To enhance algorithm robustness, a stochastic region proposing algorithm is developed to screen region proposals rather than using a predetermined threshold. The proposed algorithm is validated by recently collected grasshopper datasets. The experimental results demonstrate that the proposed algorithm not only outperforms competing algorithms in terms of average precision (0.91), average missed rate (0.36), and maximum F1-score (0.9263), but also reduces the false positive rate of recognising the existence of grasshoppers in an open field

Improving synthetic to realistic semantic segmentation with parallel generative ensembles for autonomous urban driving

Semantic segmentation is paramount for autonomous vehicles to have a deeper understanding of the surrounding traffic environment and enhance safety. Deep neural networks (DNN) have achieved remarkable performances in semantic segmentation. However, training such a DNN requires a large amount of labelled data at pixel level. In practice, it is a labour-intensive task to manually annotate dense pixel-level labels. To tackle the problem associated with a small amount of labelled data, Deep Domain Adaptation (DDA) methods have recently been developed to examine the use of synthetic driving scenes so as to significantly reduce the manual annotation cost. Despite remarkable advances, these methods unfortunately suffer from the generalisability problem that fails to provide a holistic representation of the mapping from the source image domain to the target image domain. In this paper, we therefore develop a novel ensembled DDA to train models with different upsampling strategies, discrepancy and segmentation loss functions. The models are, therefore, complementary with each other to achieve better generalisation in the target image domain. Such a design does not only improve the adapted semantic segmentation performance, but also strengthen the model reliability and robustness. Extensive experimental results demonstrate the superiorities of our approach over several state-of-the-art methods

Spectral analysis and mapping of blackgrass weed by leveraging machine learning and UAV multispectral imagery

Accurate weed mapping is a prerequisite for site-specific weed management to enable sustainable agriculture. This work aims to analyse (spectrally) and mapping blackgrass weed in wheat fields by integrating Unmanned Aerial Vehicle (UAV), multispectral imagery and machine learning techniques. 18 widely-used Spectral Indices (SIs) are generated from 5 raw spectral bands. Then various feature selection algorithms are adopted to improve model simplicity and empirical interpretability. Random Forest classifier with Bayesian hyperparameter optimization is preferred as the classification algorithm. Image spatial information is also incorporated into the classification map by Guided Filter. The developed framework is illustrated with an experimentation case in a naturally blackgrass infected wheat field in Nottinghamshire, United Kingdom, where multispectral images were captured by RedEdge on-board DJI S-1000 at an altitude of 20m with a ground spatial resolution of 1.16 cm/pixel. Experimental results show that: (i) a good result (an average precision, recall and accuracy of 93.8%, 93.8%, 93.0%) is achieved by the developed system; (ii) the most discriminating SI is triangular greenness index (TGI) composed of Green-NIR, while wrapper feature selection can not only reduce feature number but also achieve a better result than using all 23 features; (iii) spatial information from Guided filter also helps improve the classification performance and reduce noises