An Investigation into the Appropriateness of Car-Following Models in Assessing Autonomous Vehicles

Abstract

The dramatic progress of Intelligent Transportation Systems (ITS) has made autodriving technology extensively emphasised. Various models have been developed for the aim of modelling the behaviour of autonomous vehicles and their impacts on traffic, although there is still a lot to be researched about the technology. There are three main features that need to be represented in any car-following model to enable it to model autonomous vehicles: desired time gap, collision avoidance system and sensor detection range. Most available car-following models satisfy the first feature, most of the available car-following models do not satisfy the second feature and only few models satisfy the third feature. Therefore, conclusions from such models must be taken cautiously. Any of these models could be considered for updating to include a collision avoidance-system module, in order to be able to model autonomous vehicles. The Helly model is car-following model that has a simple structure and is sometimes used as the controller for Autonomous Vehicles (AV), but it does not have a collision avoidance concept. In this paper, the Helly model, which is a very commonly used classic car-following model is assessed and examined for possible update for the purpose of using it to model autonomous vehicles more efficiently. This involves assessing the parameters of the model and investigating the possible update of the model to include a collision avoidance-system module. There are two procedures that have been investigated in this paper to assess the Helly model to allow for a more realistic modelling of autonomous vehicles. The first technique is to investigate and assess the values of the parameters of the model. The second procedure is to modify the formula of that model to include a collision avoidance system. The results show that the performance of the modified full-range Auto Cruising Control (FACC) Helly model is superior to the other models in almost all situations and for almost all time-gap settings. Only the Alexandros E. Papacharalampous’s Model (A.E.P.) controller seems to perform slightly better than the (FACC) Helly model. Therefore, it is reasonable to suggest that the (FACC) Helly model be recommended as the most accurate model to use to represent autonomous vehicles in microsimulations, and that it should be further investigated.

Assessing the Impacts of Autonomous Vehicles on Road Congestion Using Microsimulation

“…Compared with this analysis, the acceleration ability of the Helly (FACC) model is superior to that of the IDM+ according to the results reported in [18]. Hence, the autonomous-vehicle speed recovery was faster than non-autonomous vehicles, as in case No.…”

“…The updated Helly model is referred to as the Helly (FACC) model. The Helly model was updated to be able to deal with the collision-avoidance system [18]. The constraints of speed and acceleration, the concept of sensor detection range, and enhancing brake function were added to the model as in Equations ( 3)-( 5) below.…”

“…This function is introduced in the model as a function of the gap settings, v i, is the speed, ∆x i (t) is the distance gap, b i and b i-1 are decelerations equal to 0.3 G (2.97 m/s 2 ), and c is 4 m. The concept of safety risk is grounded on the Mazda algorithm [19][20][21][22] and is represented in Equations ( 6) and (7). In this research, we applied the Helly (FACC) [18] model to assess the Hanshin corridor and assess the impact of using autonomous vehicles on the congestion level.…”

“…Three main requirements are essential in any model to be able to represent autonomous-vehicle behaviour: the desired time gap, the collision-avoidance system, and the sensor detection range (Table 1). Some models are limited in terms of representing the varying desired gap that is related to vehicle speed [18]. The GM model, for example, does not include the desired time gap or desired gap.…”

“…It is apparent that each model was developed for a certain case study; hence, the set of parameters are calibrated using a specific set of experimental data and therefore, their applicability to other case studies has to be justified and taken cautiously. While many studies have compared and evaluated a number of car-following models in order to assess their capabilities, there are no defined set of recommendations to date on which model is best in which circumstances; see for example [16][17][18]. Examples of car-following models include Treiber et al [5,14], who applied the IDM model to replicate the impacts of driving autonomous vehicles on traffic flows, and Suzuki et al [15], who modelled the driving behaviour of autonomous vehicles by applying the IDM+ to represent human drivers with different parameters.…”