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This study proposes a lane-changing trajectory planning method for automated vehicles under various road line-types. The method uses the polynomial regression model to describe the road line-types, and then a non-linear optimisation model is constructed to generate the lane-changing trajectory based on the road polynomial functions. The process of connecting the lane-changing manoeuvre with the car-following manoeuvre is discussed in this study, which ensures the ride comfort of the ego vehicle after the lane-changing manoeuvre. Moreover, considering that the lag vehicle on the target lane may be affected by the lane-changing manoeuvre, the situation that the lag vehicle maintains the car-following manoeuvre with the ego vehicle is taken into account in the authors’ model. Another small innovation is that they have designed a simple and effective method to find the suitable initial guess for the proposed non-linear optimisation model. The simulation results show that the lane-changing trajectory generated by the proposed model is smooth and continuous, and the automated vehicle can avoid potential collisions efficiently during the lane-changing process. In emergent conditions, the proposed model can also plan the corrected trajectory to ensure safety.

Advancement in vision‐based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such objects. However, pothole objects are difficult to identify due to their non‐uniform structure in challenging, and dynamic road environments. Existing approaches have shown limited performance for the precise detection of potholes. The study on the detection of potholes, and intelligent driving behaviour of autonomous vehicle system is little explored in existing articles. Hence, here, an improved prototype model, which is not only truly capable of detecting the potholes but also shows its intelligent driving behaviour when any pothole is detected, is proposed. The prototype is developed using a convolutional neural network with a vision camera to explore, and validates the potential, and autonomy of its driving behaviour in the prepared road environment. The experimental analysis of the proposed model on various performance measures have obtained accuracy, sensitivity, and F‐measure of 99.02%, 99.03%, and 98.33%, respectively, which are comparable with the available state‐of‐art techniques.

High-precision lane keeping is essential for the future autonomous driving. However, due to the imbalanced and inaccurate datasets collected by human drivers, current end-to-end driving models have poor lane keeping the effect. To improve the precision of lane keeping, this study presents a novel multi-state model-based end-to-end lane keeping method. First, three driving states will be defined: going straight, turning right and turning left. Second, the finite-state machine (FSM) table as well as three kinds of training datasets will be generated based on the three driving states. Instead of collecting the dataset by human drivers, the accurate dataset will be collected by the high-performance path following controller. Third, three sets of parameters based on 3DCNN-LSTM model will be trained for going straight, turning left and turning right, which will be combined with FSM table to form a multi-state model. This study evaluates the multi-state model by testing it on five tracks and recording the lane keeping error. The result shows the multi-state model-based end-to-end method performs the higher precision of lane keeping than the traditional single end-to-end model.

Radar is an important sensor to realise intelligent driving environment perception, enabling the detection of static obstacles and dynamic obstacles, and the tracking of a dynamic obstacle. The models, quantities, and installing location of the platform radar sensors as well as the information processing modules differ from each other on different intelligent driving testing platforms, resulting in different quantities and interfaces on the intelligent driving system. Here, the authors build the software architecture of intelligent driving vehicle based on driving brain which is used to adapt to different types of radar sensors and use the variable granularity road ownership radar for radar information fusion. Under the condition of complete driving information, increasing or reducing the number of radar sensors and changing the radar sensor model or installing location will not affect the intelligent driving decision directly. Therefore, the authors meet the demands of multi-radar sensor adapting to different intelligent driving hardware testing platforms.

Platooning technology is sufficiently ready that trials can be undertaken on live roads given suitable preparations and safeguards. The performance of the technology, in practice, is not well characterised and so the business case has yet to be developed. The UK can take a lead with a trial of heavy vehicle platooning; this is essential to ascertain real-world benefits. This would be subject to policy decisions. A range of organisational, legal and human factors issues need to be explored before full deployment and some need to be addressed in advance of any road trial.

The number of vehicles in the cities keeps increasing as the population increases. This poses an issue, which is to find a proper space to park. The shortage in finding a parking space results in difficulty in managing these areas as well. Traffic congestion and energy consumption is another issue that results from the above. Hence, a smart based parking system allows users to reach the nearest parking area easily. In this research, a decentralised hierarchical approach is proposed for finding a parking spot. Two levels of hierarchy are considered in this paper. The first level of hierarchy involves finding the nearest car parks which have free parking spots. The second level of hierarchy involves finding nearest parking spots in the area selected in the first level.

Presents a collection of slides covering the following topics: Greenwich automated transport environment; driverless cars; UK roads; autonomous valet parking; urban deliveries; and teleoperation.

Presents a collection of slides covering the following topics: automated vehicles; V2x communications; cooperative driving and network guided vehicle.

Presents a collection of slides covering the following topics: autonomous vehicle technologies; driver liability; insurance; UK autodrive; advanced driver aids; driverless cars; risk pricing; and cost-benefit analysis.

Vehicle emissions are significant contributors to the UK's poor air quality which is damaging our health and economic prosperity. The generation of intelligence through the combination of disparate data sources offers significant potential to inform existing and future traffic control systems to manage vehicle emissions in sensitive physical environments, or where instantaneous traffic conditions represent the greatest threat. An Adaptive Environmental Controller (AEC) is seen as a possible solution to overcome this issue.