# Adaptive Cruise Control with Look-Ahead Anticipation for Driving on Freeways
## 4 Discussion  
This paper has presented a look-ahead scheme of ACC, called La-ACC, that is easily implementable for automated driving of a vehicle aiming at better driving performance and comfort in high-speed driving. Using the standard model of an existing ACC found on the commercial vehicles, the proposed La-ACC is evaluated. The look-ahead feature is a generic technique that introduces anticipatory behavior into the ACC or car-following models to enhance its original behavior with negligible computational complexity. Therefore, the look-ahead concept is directly integrated into the car-following models. A preliminary study [34] found that the look-ahead IDM performs better than the conventional IDM in urban traffic, which flows through signalized intersections. It is expected to work well with other parametric car-following models according to our observation. Since our primary concern in this paper is to enhance the performance of ACC used in commercial cars, we do not include such investigations in this paper. However, in our future work, we would like to extensively investigate the usefulness of the look-ahead feature on different ACC or car-following models in various traffic contexts (e.g., traffic on urban roads with signalized intersections and merging scenarios). The proposed look-ahead technique should be fine-tuned further by using experimental driving, considering various factors associated with the natural driving environment. The proposed La-ACC decides the control input based on prediction such as typical MPC-based driving schemes, although their prediction horizon and technique are different. Specifically, the prediction horizon of La-ACC is only a fraction of the horizon used in MPC. Instead of apprehending the predicted states of the involved vehicles in the entire horizon in MPC, the short horizon or look-ahead step in La-ACC only provides the sense of traffic trends (e.g., slowing, steady, or speeding) to help to take cautious action (i.e., not fully optimal action). La-ACC instantly decides the control input (e.g., in a few microseconds), whereas MPC needs a numerical solution of an optimization problem that usually takes a few hundreds of milliseconds. La-ACC slightly differs from ACC because it uses the estimated acceleration of the preceding vehicle. However, the purpose of using the preceding vehicle's estimated acceleration in decision making entirely differs from CACC that aims to maintain specified gaps \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Observed Features** & **With 2.3\% ACC Vehicles** & **With 2.3\% La-ACC Vehicles** & **Change \%** \\ \hline Mean speed, km/h & 62.60 & 62.97 & +0.59\% \\ Harmonic speed, km/h & 61.04 & 61.43 & +0.65\% \\ Density, veh/km & 18.61 & 18.49 & \(-\)0.63\% \\ Travel time, sec/km & 60.04 & 59.67 & \(-\)0.61\% \\ Delay, sec/km & 14.51 & 14.15 & \(-\)2.53\% \\ Stop time, sec/km & 1.99 & 1.96 & \(-\)1.62\% \\ Mean queue, veh & 0.38 & 0.37 & \(-\)4.03\% \\ Fuel efficiency, km/1 & 14.66 & 14.90 & +1.63\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The overall performances of traffic: comparison of La-ACC over ACC in mixed traffic. and speeds for all vehicles in the platoon. Instead of applying the part of the acceleration of the preceding vehicle directly such as in CACC, La-ACC does not use such acceleration directly in the decision making but in determining the predicted state of the preceding vehicle. This distinct feature makes La-ACC more flexible than CACC and applicable to controlling individual vehicles. In a fully connected vehicle environment, information from the multiple preceding vehicles will be available readily to better predict the behavior of the immediate preceding vehicle using some car-following models. In such a scenario, the accuracy of the look-ahead state could be better, resulting in the improved performance of La-ACC.