Analysis and modeling of human driving behaviors using adaptive cruise control

Applied Soft Computing 1 (2001) 237–243

Industrial report

Analysis and modeling of human driving behaviorsusing adaptive cruise control

Hiroshi Ohno∗Toyota Central R&D Labs., Inc., Nagakute Aichi, Japan

Received 9 March 2001; received in revised form 31 July 2001; accepted 23 August 2001

Abstract

We describe a driver model based on the feedback-error learning scheme using neural network (NN) for adaptive cruisecontrol (ACC) use in driving and show the applicability of the feedback-error learning scheme as a behavior model ofadaptability of human. The focus of the study is on the adaptation process of driving behaviors using ACC. The drivermodel for computer simulations was implemented by using a NN. The developed simulation model is used for predictingcontrol performance of skilled driver using ACC. In the experiments, we used a fixed-base driving simulator (DS) installedACC system for collecting driver’s data. Headway time when lane changing in a row and lateral deviation from road centerwere investigated as driver behavior characteristics during ACC use and manual driving, respectively. The simulation resultsof the lateral deviation were compared with the experimental results and showed that control performance with ACC usewould be better than that of manual driving. Furthermore, it was found that human error occurred during ACC use in the DSexperiments and provided an explanation of the cause in terms of Neisser’s perceptual cycle model. © 2001 Published byElsevier Science B.V.

Keywords: Driver model; Adaptive cruise control; Neural network; Feedback-error learning; Neisser’s perceptual cycle

1. Introduction

Vehicle automation is likely to be perceived fromthe point of view of driver comfort and safety. Atpresent, adaptive cruise controls (ACCs) exist in pro-duction vehicles. ACCs are capable of automaticallymaintaining a constant inter-vehicle distance andspeed, which are selected and set by the driver. Thus,according to the behavior of other vehicles, e.g. a leadcar, the proper speed is automatically adapted whilemaintaining a safe inter-vehicle distance. Therefore,

∗ Tel.: +81-561-63-4699; fax:+81-561-63-5743.E-mail address: [email protected] (H. Ohno).

the driver only needs to be attentive to steeringmanoeuvers.

ACC systems have been developed and evaluatedboth on the road and in driving simulators (DSs) ascomfort and safety enhancing systems [1,9,10], and[15]. Some studies suggested that ACC system wouldadd comfort and convenience to the driver. Thesehave been treated as the skill objectives for ACC usein the experiments. However, it may be difficult todetermine the degree of skillfulness of participantsdue to their individual differences. Furthermore, itis difficult to continue the experiments over longperiods such as prolonged driving because of fa-tigue or simulator sickness, either on the road orin a DS.

1568-4946/01/$ – see front matter © 2001 Published by Elsevier Science B.V.PII: S1568-4946(01)00022-9

238 H. Ohno / Applied Soft Computing 1 (2001) 237–243

In this paper, we illustrate the application of neuralnetwork (NN) for the prediction of the control per-formance of skilled behavior with ACC use. First, weanalyze driver’s behavior characteristics in the earlystage of ACC use in the DS. The DS is a fixed-basedriving simulator. The headway time when lane chang-ing in a row and lateral deviation were investigated.For the explanation of mechanism of human factors,we introduce a cognitive framework for driver’s infor-mation processing. The cognitive framework can helpto interpret the driver behaviors. Second, the simu-lation model is implemented by using an NN, whichis based on the feedback-error learning scheme [5]and it investigated control performance, e.g. lateraldeviation from road center, for the purpose of predict-ing the control performance of the skilled behavioron ACC use. Finally, we describe the future workin the development of advanced transport telematicssystems.

2. Cognitive framework

From the information processing point of view,it appears that there exists the difference betweenplant operator such as in power plants and driver. Theplant operator performs mainly statically operationsthat are considered as passive reactions accordingto the external events. Whereas the driver performsdynamical operations from the points as follows:(1) picking up the available information for driv-ing to compensate for the time delay of control. (2)Adapting to the environmental changes. Thus, the

Fig. 1. Cognitive framework.

model for human information processing has a needto reflect the dynamical properties according to theenvironmental changes. As a typical cognitive frame-work, Rasmussen’s skill-rule-knowledge based model(SRK model) [12] is well known. This has its focuson the plant operator and may be suitable to decidethe behavior category at a fixed situation. Therefore,we adopt another cognitive framework for the drivermodel that is Neisser’s perceptual cycle [7], which in-cludes schemata, visual search in terms of a schema,and real world and thus suits the description of theclosely relation between agent and environment.Neisser’s perceptual cycle has dynamical propertiessuch as adaptation and learning in a natural way. Asshown in Fig. 1, we summarize the cognitive frame-work for power plant, airplane, and car, respectively.The airplane is positioned at the middle point be-tween power plant and car since flight operationsinclude both supervisory tasks in automated aids andmanual tasks such as take-off and landing. From theleft to the right in the figure, the relationship betweenperception and action becomes closer.

3. Method

The experiments using DS and the simulationexperiments are performed for the analysis and pre-diction of control performance, respectively. In thesimulation experiments, we adjust the learning rate ofthe simulation model according to the results of theexperiment. The DS is a fixed-base driving simu-lator that has three screens at the front. Five males

H. Ohno / Applied Soft Computing 1 (2001) 237–243 239

Fig. 2. Neural network based driver behavior model with ACC use.

experienced drivers between 25 and 35-year-oldparticipated in the study. All participants had helda driving license for more than 5 years and had notexperienced ACC system.

3.1. Simulation model of driver behaviors

The main issue of driver behavior modeling withrespect to the adaptation process is real-time adapt-ability for control and learning simultaneously. Ac-cordingly, we adopted the feedback-error learningscheme [5], which is a computational model for thecerebellum learning in brain, and constructed thesimulation model as shown in Fig. 2.

The driver behavior model consists of an NN andtwo feedback controllers that are the proportional con-troller. The NN is the three-layered feed-forward typebecause of an excellent universal approximator andhas a sigmoid type activation function for the units inthe hidden and output layers, 2/(1 + exp(−x)) − 1.The advantage of the scheme is that it performs bothcontrol and learning simultaneously. The outputs ofthe model are the velocity and the steering angle ofcar. These consist of the output of the feedback con-troller and the output of the NN as shown Fig. 2. Thelearning algorithm used the backpropagation method[14]. In the feedback-error learning scheme, the error

signals of the backpropagation are the feedback sig-nals as follows:

�w = εdo

dwofb

where�w is the change of weight values in NN,ε isthe learning rate,o is the output of NN, andofbis thefeedback signal that is the output of the proportionalcontroller. After learning, since the feedback signalsare equal to zero, the control is performed only by thefeed-forward controller in terms of NN. The inputs ofthe NN are road curvature (at 100 m forward), velocity,the difference between the desired and actual distancefor a lead car, steering angle, lateral deviations (at2, 15 and 20 m forward, respectively) from the roadcenter (e.g. [6–8]), and the on–off signal for ACC use.These are normalized in the range of from−1 to 1. Inlearning when ACC is used, the target of the output ofthe velocity in the NN holds at zero, during which thefeedback signal with respect to the velocity is cut off.

For preventing the problem of overfitting in learn-ing, we applied the overload learning method [11]to the feedback-error learning. In overload learning,the input and the target of the output of NN hold anidentical value that is a uniformly random value from−1 to 1, which enables the realization of identicalmapping in NN.


3.2. ACC system in driving simulator

The ACC system installed in the DS controls thevelocity and distance between the driver’s own carand a lead car by using the accelerator throttle. Thesimulated distance sensor had a detection range of toabout 110 m. The operation of the system was pos-sible in the velocity interval from about 40 km/h toabout 110 km/h. The human-machine interface of theACC system, such as visual information and auditorywarning, was omitted.

3.3. Driving tasks

The test route for the DS was a two-lane high-way. Each lane was 3.75 m wide. The road surfacewas asphalt with a randomized texture. Visibility wasabout 300 m. The driving tasks were as followed:participants followed the lead car under manual orACC control; lane-changed in a row when the leadcar was suddenly decelerated; and had to overtakethe other lead car. In the tasks the following condi-tions, T1: manual driving× ACC driving after lanechanging, and T2: ACC driving× manual drivingafter lane changing, were performed. In one session,T1, T1, T2 and T2 were done in succession. The totalnumber of sessions was seven. The driving time perone session was about 30 min. All participants hadthree test driving times for the manual driving forpractice.

Fig. 3. Experimental results of headway time when lane changing in a row.

4. Experimental results

4.1. Headway time

The headway time was calculated as the distancebetween the own car and the lead car divided by theown car’s velocity. Three participants had a longerheadway time when the ACC was used. Fig. 3 showsthe headway time profile for one participant in thiscase. Thex-axis is the number of trials. They-axisis the headway time. Another two participants had alonger headway time in the initial trial, and a shorterone afterwards when the ACC was used.

Moreover, one participant hardly braked to avoida collision when lane changing in a row. This washuman error [13] in the skill-based level (in theRasmussen sense), such that the judgement for lanechanging was delayed. The participant said that ACCdriving was comfortable, so he didn’t notice theapproaching lead car because he was thinking of otherthings. The situation awareness [2] became lower thanthat of manual driving. Thus, the automation systemssuch as ACC system tend to lead to loss of situationawareness [3]. The explanation of this human errormechanism using Neisser’s perceptual cycle is giventhat, during ACC driving, the visual search becomesneglected due to the distance control provided bythe ACC system, and the schema regarding collisionavoidance is not activated, then participant cannotavoid a collision. Stanton et al. [16] also reported


Fig. 4. Experimental results of lateral deviation profile.

that a third of the participants was unsuccessful inreclaiming control of the vehicle before a collisionoccurred. Therefore, it may be necessary for ACCsystems to provide the collision warning system andautomatic braking system to avoid a collision.

4.2. Lateral deviation

Three participants resulted in a larger lateral devia-tion in the initial trial when ACC was used. The resultfor one participant is shown in Fig. 4. In the figure,the regression curves are included. Thex-axis is thenumber of trails. They-axis is the lateral deviation.The solid line is the manual driving. The dashed lineis the ACC driving. From the regression curves, it isfound that the larger the trail is, the smaller will bethe lateral deviation.

5. Computer simulation results

As shown in Fig. 4, in the initial stage of trials, thelateral deviation in the case of the ACC driving waslarger than that of the manual driving. For the sakeof reflecting this feature in the simulation model, weadjusted the learning rate of the simulation model. Inthe simulation experiment, the following conditions,S1: learning of manual driving, and S2: learning ofmanual driving or alternatively ACC driving, wereperformed. In S2, the initial weights were set by theresulting weights in S1. The test course is shown in

Fig. 5. The initial velocity for both the own car andthe lead car was 10 m/s. The initial distance was 10 m.The driving time was 16 s. The desired distance ofthe ACC system was 9 m. The simulation cycle was10 ms and the control cycle was 100 ms. The learningrate was 0.001. The momentum term was 0. The ini-tial weights in S1 were set at uniformly random valueswithin the range of from−0.001 to 0.001. The num-ber of hidden units was 30, which resulted in a goodfeed-forward model at a certain trail.

The results at the five different random seeds wereaveraged. In S1, the number of learning epochs was500, i.g., 500 trials. Fig. 6 shows the results in S2,performed up to 80 trials. The initial trails resulted in alarger lateral deviation in the case of the ACC driving,and the larger the trail is, the smaller is the lateraldeviation. These results coincided with the results ofthe experiment as shown in Fig. 4. Therefore, it reveals

Fig. 5. Test course for simulation experiments.


Fig. 6. Simulation results for condition S2.

Fig. 7. Simulation results for condition S2.

that the feedback-error learning scheme using NN cancapture the adaptability of driver’s behavior.

The results for from 500 trials to 16,000 trails areshown in Fig. 7. The thick line shows the results withoverload learning. The thin line shows the results forwithout overload learning. It appears that the lateraldeviation in the case of the ACC driving is smallerthan that of the manual driving, and the problem ofoverfitting in learning was not found.

6. Conclusion

We described the analysis and modeling of driverbehaviors using ACC control and showed the applica-bility of the feedback-error learning scheme using NNas a behavior model of adaptability of human. An NN

based simulation model was developed for predictingthe control performance in the adaptation process overlong periods. The experimental results in the DS andthe computer simulations are as follows: (1) headwaytime was longer when the ACC was used in the DSexperiment. Therefore, ACC systems may lead to areduction in accidents. (2) The lateral deviation in thecase of the ACC driving in the initial stage increasedmore than that of the manual driving. (3) Human er-ror occurred when lane changing in a row during ACCdriving for one participant. The explanation of the hu-man error mechanism was given in terms of Neisser’sperceptual cycle. (4) The lateral deviation in the caseof the ACC driving will be ultimately smaller than thatof the manual driving, as based on the results of thesimulation model. Regarding this result, the furtherstudy on the behavior adaptation [4] may be needed. In


order to achieve safety and comfort driving, the futurework needs to develop backup system for preventinghuman error and on-line assessment method of situa-tion awareness or workload [17] during driving.

Acknowledgements

The author thanks Prof. Furuhashi of Mie Univer-sity, Prof. Sawaragi of Kyoto University, and theanonymous reviewer for much help in improvingearly versions of this paper.

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Analysis and modeling of human driving behaviors using adaptive cruise control

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