# TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles
## 1 Introduction  
Connected and autonomous vehicles (CAVs) are emerging as next-generation transportation in terms of growing connectivity and automation devices in vehicles [1, 2]. Most modern cars have a dashboard and connectivity to the internet or personal devices enabling a wide range of traffic services for easing driving and travel for drivers [3]. Nowadays, the majority of the drivers use GPS service travel pathfinder, which helps in reaching the correct destination particularly significant in urban dense road networks [4, 5]. Internet connectivity enabled traffic services to not only improve safety and travel experience but also resulted in green transport, considering lesser congested and shorter travel path-oriented guidance to drivers [6]. The green transport capability of modern vehicles has attracted government attention around the work towards supporting CAV technology development and implementation in coming years [7, 8]. In line with this, the UK department of transport has supported various R&D projects related to the advancement of CAV [9]. Various enabling technologies have been developed to support the on-road realisation of CAV for public and private transport [10]. The early advancements in mobility planning for CAVs were majorly based on sensor-oriented predictions [11]. For example, a vision centric steering control and path planningwas suggested for autonomous vehicles focusing on polynomial interpolation problem-based modelling of the on-road traffic environment [12]. A point-by-point vehicle movement plan and iterative steering supervision strategy were developed relying on on-road real-time vision or images. Towards enhancing the vision-based path planning for CAVs, manoeuvres-based path planning was suggested focusing two-level of information gathering from the on-road traffic environment [13]. In the first level, information gathering focused on the feasibility of manoeuvres considering the traffic scenario whereas in the second level optimisation of manoeuvres has been performed in terms of various performance metrics, including transportation time, rules of traffic, fuel consumption and ease of travel. For incorporating some complex steering control situations, such as sliding effects of tires, a new autonomous vehicle control system was suggested considering slip angles of the vehicle in kinematic modelling of the path following traffic environment [14]. The aforementioned advancements heavily relied on sensor data-centric mobility planning in on-road traffic environments without applying any learning from historical scenarios or data. The recent advancement in advanced machine learning has enabled deep learning techniques to be applied in diverse domains [15; 16] including mobility planning for CAVs. For example, deep neural network-based calibration automation has been suggested for improving the accuracy of sensor centric distance calculation [17]. Specifically, stereo matching and lidar projection-based path planning for autonomous vehicles were enhanced by modelling calibration network and loss function. However, the accuracy of the calibration automation approach is highly reliant on the accuracy of sensor data of autonomous vehicles without considering the overall traffic environment. Towards addressing the sensor-based path planning, a reinforcement learning-based car following model has been suggested focusing on video frame analysis [18]. In particular, you look, once the strategy has been developed, for identifying leader vehicles and obstacles in the car-following traffic environment. Q-learning and deep q-learning have been used for autonomous vehicle path planning considering red green blue depth frames in the on-road traffic video analysis. Similarly, for improving the autonomous vehicles' consumer experience, the deep learning-based caching mechanism has been suggested focusing on edge-based media execution [19]. However, autonomous vehicle decision making was not considered in the media-oriented study.