# Adaptive Cruise Control with Look-Ahead Anticipation for Driving on Freeways
## 1 Introduction
Various advanced automated vehicle control systems, e.g., MPC, have been investigated recently to overcome the limitations of traditional driving or ACC [26; 27; 28; 10; 29]. Comfortable and efficient driving behaviors can be realized by anticipating the traffic ahead using information via inter-vehicle communication and taking appropriate control action to drive a vehicle smoothly and improve traffic flows [30; 31]. In these MPC-based driving systems, using the predicted state of the preceding vehicle, an optimization problem is solved to obtain the control input of the vehicle. Hence, the solution provides anticipatory car-following behavior that improves the driving efficiency of a vehicle by dynamically tuning spacing and speed. In contrast to these advanced vehicle control, traditional ACC decides the vehicle control input according to the current state of the preceding vehicle; therefore, the vehicle cannot avoid aggressive braking often in varying traffic flow conditions [10; 22]. Due to high computational cost and complexity in obtaining reliable solutions, MPC is not easy to implement on vehicles despite a potential comfort gain for the driver and environmental benefits. Therefore, the use of these advanced driving systems remains limited mostly in theoretical and simulation studies. Despite being a decades-old technology, none of the existing ACC found on commercial cars uses the advanced optimization-based decision technique. A very recent experimental study on seven different 2018-model-year vehicles with ACC reveals that none of these vehicles are even string stable, i.e., the ACCs can operate the vehicles only at steady speed above some high level and need to be deactivated in complex maneuvers [32]. Pointing out the potentials and limitations of the predictive optimal control schemes, a simple look-ahead car-following scheme using the intelligent driver model (IDM) [33] was proposed that takes control decision by predicting the behavior of the preceding vehicle [34]. A detailed investigation has been conducted recently by applying such a look-ahead car following with the existing driving schemes to observe their impacts on the fuel consumption, the overall traffic flow, and utilization of the intersection at various penetration rates [35]. In the context of signalized intersection and low-speed driving on the urban roads, the model of commercial ACC with such a look-ahead scheme does not perform well due to its frequent and long deactivation periods. Therefore, how anticipatory driving features influence the operating scope and the performance of existing ACC on the freeway with designed operating speed have not been investigated yet. This paper presents a look-ahead scheme of ACC by extending the decision process of an existing ACC available on commercial high-end cars. In the context of freeway driving, the future position and speed of the preceding vehicle after a short period are predicted using a conditional persistence prediction technique and then used to decide the control input of the vehicle by using the model of a commercial ACC that was calibrated using experimental driving data [22]. Although the look-ahead feature of the proposed ACC is inspired by the MPC-based driving scheme, it completely differs from MPC in the sense that the driving decision is taken in real time using parametric functions instead of rigorous numerical optimization. Therefore, the main contribution is the look-ahead framework that connects prediction and decision-making procedures to the existing ACC employing parametric functions (i.e., without involving numerical optimization) for real-time computation to provide a partly similar behavior pattern of the MPC schemes to some extent. A time-varying prediction horizon and the conditional persistent prediction technique were introduced to keep the look-ahead predicted states reliably accurate despite varying traffic conditions.