# TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles
## 2 Related Work  
The advancements in mobility planning for CAVs can be divided into two major sub-themes including sensor-enabled predictions and advanced machine learning enabled predictions [20; 21]. Towards sensors enabled predictions, a vision centric steering control and path planning was 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. However, vision centric steering control is limited in terms of overall traffic knowledge-centric movement planning considers not only neighbouring vehicles rather whole traffic environments in a larger area. Similarly, manoeuvres-based path planning was suggested focusing on two levels 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. However, manoeuvres-based path planning is lacking real-time efficiency criteria considering level-wise execution as some sensors' input needs to process immediately rather than waiting for other sensors to calibrate. A new autonomous vehicle control system was suggested considering slip angles of vehicles in kinematic modelling of the path following traffic environment [14]. However, the slip angle-based framework is limited in terms of applicability in sparse traffic environments due to the dependence on neighbouring vehicles.  
Towards advanced machine learning-enabled predictions, 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. A reinforcement learning-based car following model has been suggested focusing on video frame analysis [18]. Here, 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 and blue depth frames in the on-road traffic video analysis. Similarly, a deep learning-based caching mechanism has been suggested focusing on edge-based media execution [19]. The multi-sensor data-fusion algorithm has been developed using unscented Kalman filter to compute improvised Unmanned Surface Vehicle (USV) operation in a practical environment [22]. The robust fuzzy sliding mode rule has been applied to guide the Autonomous Underwater Vehicles (AUV) [23]. Here, both sensors enable path planning and machine learning-enabled path planning [24,25] learning from historical scenario data and traffic knowledge sharing is lacking among autonomous vehicles in the same traffic environment which is the core target of this research detailed in the following sections.