2.1. Speeding behavior

The proposed functionality IVDM activates the ACC feature when triggered; thus, the ACC controller automatically estimates the parameter LOT based on the ACCSSP (Labuhn and Chundrlik, Reference Labuhn and Chundrlik1995) and possesses the capability to reduce the vehicle speed while encountering a host vehicle in the defined proximity (Marsden et al., Reference Marsden, McDonald and Brackstone2001). The ACC feature is a primary component of ADAS which can be activated for any speed $ \ge $ 25 MPH, resulting in augmented safety and vehicle engine performance (Luo et al., Reference Luo, Liu, Li and Wang2010). Thus, IVDM predicts ACCSSP based on the type of road segment (Supplementary Table S8) and further details were discussed in Section 6 (Kolachalama and Malik, Reference Kolachalama and Malik2021a,Reference Kolachalama and Malikb).

2.2. Steering behavior

The steering behavior vector (SBV) consists of four elements (speed, LAT, YAR, radius of road curvature [RRC]), and the parameter RRC is known from the AICON feature based on the global position system (GPS) coordinates. The measure of hard braking and acceleration (Wåhlberg, Reference Wåhlberg2007) was extended by introducing the concept of ideal steering behavior (ISB) (Kolachalama et al., Reference Kolachalama, Hay, Mushtarin, Todd, Heitman and Hermiz2018). Hence, IVDM would incorporate ISB and the guidelines established by the United States transportation authority to estimate [LAT, YAR] for definite RRC (Kolachalama and Lakshmanan, Reference Kolachalama and Lakshmanan2021). The ISB was defined assuming no lateral or longitudinal slip, and the corresponding mathematical models were shown in Section 4.1.

2.3. Cabin air temperature

In an internal combustion engine vehicle, the thermal energy produced by the fuel combustion is exchanged between the environment (external air temperature [EAT]) and the vehicle (Borman and Nishiwaki, Reference Borman and Nishiwaki1987). Among the vehicle’s many features, the HVAC is the only system that maintains the set cabin air temperature (CAT), driven by user command. The CAT is increased by extracting heat from the engine surface and decreased by the known process of HVAC. Hence, IVDM proposes a model that can generate an optimal CATSP, potentially augmenting the efficiency of HVAC and Engine (Kolachalama and Malik, Reference Kolachalama and Malik2021a,Reference Kolachalama and Malikb).

3.1. Vehicle level vectors

The VLV include three components, namely, body module, driver behavior, and environmental factors. The body module vector is embedded with the age of the vehicle (time step, odometer), tire pressure, the shape of the vehicle (aerodynamic drag), and load (trailer, passengers). The tire pressure and external load affect the normal and traction forces exerted on the wheels, affecting the vehicle engine performance (Liang et al., Reference Liang, Ji, Mousavi and Sandu2019). The body design influences aerodynamic resistance and instantaneous fuel consumption rate (IFCR 1E-8 m³/s) (Sudin et al., Reference Sudin, Abdullah, Shamsuddin, Ramli and Tahir2014), whereas the DBV consists of the elements discussed in Section 2. The environmental factors consist of interactive vehicle elements while traversing any terrain; EAT influences the Engine’s thermal stress (Kolachalama et al., Reference Kolachalama, Kuppa, Mattam and Shukla2008), whereas the terrain data (curvature and gradient) obtained from the AICON features affect vehicle dynamics. Also, the gradient is proportional to the vehicle’s Euler angles; hence, there is no loss of generality in considering pitch, roll, and yaw angles as inputs replacing the gradient (Eathakota et al., Reference Eathakota, Singh, Kolachalam and Krishna2010). The parameters steering angle, humidity (HUM), and atmospheric pressure (ATP) were not included in this research as the data has no significant variance, and the effect of these elements on vehicle engine performance is minimal. The real-time analysis was performed under no-slip conditions, that is, the traction force generated at the wheels is proportional to the normal forces (Eathakota et al., Reference Eathakota, Kolachalama, Krishna and Sanan2008).

3.2. Engine operating point

The only external input in an internal combustion engine-driven vehicle is the air-fuel mixture ignited to produce downward thrust onto the piston surface. The flame impingement produces instantaneous engine torque (IET Nm) and is transmitted to the engine components, which results in instantaneous engine speed (IES rad/s). It is a well-established industrial methodology to represent the EOP with the three parameters IET, IES, and IFCR projected on the engine map generated for every vehicle (Kolachalama and Lakshmanan, Reference Kolachalama and Lakshmanan2021). Thus, optimal EOP was considered as the criteria to predict ACCSSP, whose details are presented in Section 4.2.

3.3. HVAC parameters: CATOP

The HVAC functionality is driven by multiple parameters: engine fan speed, load, air conditioning refrigerant fluid pressure (ACRFP), engine surface temperature (EST), and power consumed. The analysis of data retrieved resulted in substantial variations of [EST (°F), ACRFP (PSI)] with changes in CAT (°F) and EAT (°F), thus remaining HVAC elements were not considered in this research. The vector [EST (°F), ACRFP (PSI)] is empirically termed as CATOP, and optimal criteria to generate the CATSP was defined in Section 4.3. The snippets of HVAC elements were collected at a frequency of 10 m (odometer reading), as it was observed that the controller area network bus would require at least 300 ms to record variations in CAT (°F) during the steady-state (Kolachalama and Malik, Reference Kolachalama and Malik2021a,Reference Kolachalama and Malikb).

4.1. Ideal steering behavior

The steering behavior of the vehicle was estimated by the concept ideal steering behaviour (ISB) using the steering behaviour vector [SBV] (speed, LAT, YAR, RRC) (Kolachalama and Malik, Reference Kolachalama and Malik2021a,Reference Kolachalama and Malikb). The mathematical model of ISB was defined by Equations (1) and (2), assuming no lateral and longitudinal slip. Equation (1) is a quadratic function relating the parameters of SBV, and Equation (2) is the linear optimization function (LOF) framed to resolve Equation (1). Therefore, [LAT ( $ {L}_a $ ), YAR ( $ {Y}_a $ )] = [0, 0] for high RRC (~ $ \infty $ ), and it is easy to see that LOF (~0) has an infinite set of solutions that satisfy the constraint $ \mathrm{RRC}\cdot {Y}_a^2={L}_a $ . Hence, the parameters [LAT, YAR] could be estimated if the speed value $ \left({V}_s\right) $ is known, and the details were presented in Section 6:

(1) $$ 2 RRC\hskip0.70em =\hskip0.70em \frac{V_s^2}{L_a} + \frac{V_s}{Y_a},{V}_s\hskip0.35em =\hskip0.35em \frac{-{L}_a+\sqrt{L_a^2+8 RRC\cdot {L}_a\cdot {Y}_a^2}}{2.{Y}_a}, $$

(2) $$ LOF\hskip0.35em =\hskip0.35em min\;\left[ abs\left({Y}_a\cdot {V}_s-{L}_a\right)\right]. $$

4.2. Engine performance

This research measures the Engine’s capability by three parameters (engine torque caliber [ETC], engine speed caliber [ESC], Euclidean distance [ED]), directly related to vehicle engine performance. These parameters represent the torque produced per unit of fuel consumption (ETC), speed produced per unit torque (ESC), and the ED of the EOP from Ideal EOP under normal driving conditions. An engine map is a traditionally accepted convolute graph in the industry, plotted with operating EOP’s calibrated at the manufacturing plant. The coordinate with the lowest IFCR (1E-8 m³/s) was assumed to be the ideal EOP, and the line segment conjoining the operating and ideal EOP was empirically defined as the vehicle engine performance vector. In Sections 5 and 6, the predictive model of [EOP, ACCSSP] was discussed, and Figure 4b is the pictorial representation of the instantaneous engine map (IEM), categorizing two ACCSSP profiles (predicted and constant). The magnitude of the vehicle engine performance vector represents the ED (Equation (3)), and IES (rad/s) was ignored in estimating the ED, as higher IES is desired to reduce the trip time (Kolachalama and Malik, Reference Kolachalama and Malik2021a,Reference Kolachalama and Malikb).

Figure 4.

(a) Engine map: 2007 Toyota Camry 2.4 L I4 and (b) IEM—vehicle engine performance vector (Kolachalama and Malik, Reference Kolachalama and Malik2021a,Reference Kolachalama and Malikb). Source: Ricardo baseline standard car engine: Tier 2 fuel. EPA ALPHA vehicle simulations. Version: June 20, 2016. The engine map for the 2007 Toyota Camry 2.4 L I4, whose ideal EOP = [170 Nm, 2,400 RPM, 230 g/kwhr], was shown in (a). The conversion to the SI units was performed assuming the [Calorific value ( $ {C}_v $ ), density ( $ {\rho}_f $ )] = [45 MJ/kg, 750 kg/ $ {\mathrm{m}}^3 $ ], and thus the ideal EOP = [170 Nm, 251.33 rad/ $ {\mathrm{s}}^1 $ , 159 1E-8 m³/s]. A 2020 Cadillac CT5 test vehicle was utilized in the current research (Section 6), whose ideal EOP was assumed to be [250 Nm, 140 rad/s, 180 1E-8 m³/s].

Vehicles traversing arterial road segments with speed limits ranging [25 45] MPH have operating EOP closer to the ideal EOP (lower ED), as shown in the engine map (Figure 4a). The speeds of the vehicles on freeways range [65 85] MPH, which correspond to higher IES, and fluctuating [IET, IFCR] depending on the dynamic state of the vehicle. Also, the state ways with speed limits (SL) as [45 65] MPH are considered the green zone (low IFCR). Hence, the generic criteria for augmented Engine operating conditions would be lower [ED, IFCR] and higher [IET, IES, ETC, ESC]:

(3) $$ ED\hskip0.35em =\hskip0.35em \sqrt{\;{\left({IET}_i-{IET}_k\right)}^2+{\left({IFCR}_i-{IFCR}_k\right)}^2,} $$

(4) $$ \mathrm{ETC}\hskip0.35em =\hskip0.35em \frac{IET}{IFCR}, $$

(5) $$ \mathrm{ESC}\hskip0.35em =\hskip0.35em \frac{IES}{IET}. $$

4.3. HVAC criteria: CATOP

In this research, the elements [Engine surface temperature (EST), air conditioning refrigerant fluid pressure (ACRFP)] were defined as CATOP, and the optimal thermal stress and engine oil viscosity on the engine components result when EST ( $ {EST}_i $ ) = 194°F (Borman and Nishiwaki, Reference Borman and Nishiwaki1987). The parameters A1 and A2, shown in (6) and (7), represent the conformance between the operating EST ( $ {EST}_o $ ) and ideal EST ( $ {EST}_i $ ). Hence, minimum values of A1 and A2 are the criteria for optimal HVAC. The retrieved data are shown in Supplementary Tables S1–S7, which depict the recorded EST ranges [165 220]°F.

The refrigerant integrated into the air conditioning system (ACS) of the Cadillac vehicle was assumed to be R134a, and augmented functionality of ACS was achieved by limiting the maximum value of operating ACRFP ( $ {\mathrm{ACRFP}}_o $ ). The upper boundary limits of ACRFP ( $ {\mathrm{ACRFP}}_h $ ) were defined in Supplementary Table S10 in correlation with the EAT, and the intermittent boundary values of ACRFP (PSI) for EAT = [65 110]°F were estimated by basic linear interpolation. Therefore, minimum B defined in (8) and (9) was considered the optimal HVAC criterion corresponding to ACRFP (PSI) (Kolachalama and Malik, Reference Kolachalama and Malik2021a,Reference Kolachalama and Malikb). Thus, B is always nonnegative when EAT $ \ge $ 65°F, but when EAT <65°F, the parameter B is not significant in our analysis:

(6) $$ A1\hskip0.35em =\hskip0.35em \left({EST}_o-{EST}_i\right),\hskip0.5em if\;{EST}_o>{EST}_i\hskip0.35em =\hskip0.35em 194{}^{\circ}\mathrm{F}, $$

(7) $$ A2\hskip0.35em =\hskip0.35em \left({EST}_i-{EST}_o\right),\hskip0.5em if\;{EST}_o\le {EST}_i\hskip0.20em =\hskip0.35em 194{}^{\circ}\mathrm{F}, $$

(8) $$ B\hskip0.71em =\hskip0.35em \left({ACRFP}_o-{ACRFP}_h\right),\hskip0.5em if\;{ACRFP}_o>{ACRFP}_h\left(\mathrm{PSI}\right), $$

(9) $$ B\hskip0.71em =\hskip0.35em 0,\hskip0.5em if\;{\mathrm{ACRFP}}_o\le {\mathrm{ACRFP}}_h\;\mathrm{or}\; EAT<65{}^{\circ}\mathrm{F}. $$

4.4. Smoothness measure: Vehicle engine performance

The elements of the EOP were analyzed and it was observed that IES (rad/ $ {\mathrm{s}}^1 $ ) is a smoother curve, whereas [IFCR, IET] have fluctuating behavior. The flame propagation phenomena due to the ignition of the fuel-air mixture trigger thrust and torque with oscillating magnitudes. Hence to minimize the vibrations, techniques include optimizing the spark ignition timing and camshaft mechanism (Kakaee et al., Reference Kakaee, Shojaeefard and Zareei2011), integrating flywheel and generator (Gusev et al., Reference Gusev, Johnson and Miller1997) were adopted. Therefore, smoothness measure vector (SMV) [ $ {R}^2 $ , adjusted $ {R}^2 $ , sum of square errors (SSE), root mean square error (RMSE)] of the parameters—[EOP, CATOP] and [ED, ETC, ESC] was considered in our analysis, and the vehicle engine performance criteria were shown in Supplementary Table S11 (Kolachalama and Malik, Reference Kolachalama and Malik2021a,Reference Kolachalama and Malikb). The SMV was estimated using the built-in toolboxes of MATLAB, and the spline function was utilized to fit the data points.

5.1. NARX and LSTM methods: Modeling

The default properties built-in MATLAB were utilized to initialize the process, as shown in Supplementary Table S12, and DL models were developed using m-script. The NARX is a recurrent dynamic network whose mathematical model predicts the future output steps by regressing the previous states of output and exogenous (independent) inputs. In contrast, LSTM predicts the output by considering the long-term dependencies of the entire set of inputs and possesses the properties of recurrent neural networks (Kolachalama and Lakshmanan, Reference Kolachalama and Lakshmanan2021). LSTM produced the best results for classification and regression of biological data (e.g., antibody sequencing), and NARX is preferred for the data with nonlinear behavior. The performance analysis of the predictive model of EOP using NARX and LSTM DL methods was done by varying the parameters (training size, test size, hidden layers/units), and it was proven that the NARX method outperformed LSTM for ACC activated and deactivated datasets (Kolachalama and Lakshmanan, Reference Kolachalama and Lakshmanan2021). In this section, extended validation was performed using a similar methodology for the datasets depicted in Supplementary Tables S1–S7.

5.2. NARX and LSTM methods: Performance analysis

The resulting plots compare the retrieved data (blue) and predicted values (orange) using [NARX, LSTM] methods for randomly selected snippets as shown in Supplementary Figures S1–S6 (NARX) and Supplementary Figures S7–S12 (LSTM). The first row of the figure represents the three elements of EOP, and the units of [IET, IES] were Nm and rad/s, whereas IFCR was recorded on a scale of 1E-8 m³/s. The second row consists of the CATOP vector [Engine surface temperature (EST), air conditioning refrigerant fluid pressure (ACRFP)], measured in [Fahrenheit (°F), pounds per square inch (PSI)]. The numerical performance of the developed deep learning (DL) models was validated by adopting traditional statistical measure vector (STMV) = [root means square error (RMSE), first-order derivative (FOD), signal-to-noise ratio (SNR)] on the conformance between actual and predicted values, as shown in Supplementary Tables S13 and S14. The NARX method produced maximum RMSE IET = 2.465 (CT4—Set 1), whereas LSTM network produced minimum RMSE IET = 18.515 (XT6—Set 2). The element IES was predicted with equal competence by NARX (FOD < 1.129) and LSTM (FOD < 1.42), but LSTM lacked the required consistency savvy (mean IFCR FOD = 11.9) to match the NARX output (mean IFCR FOD = 10.22) for all the datasets. Similarly, the NARX prediction had 75% lower RMSE EST and 18% lower FOD ACRFP when compared to LSTM output. It is easy to see that, despite the stochastic variation, the predicted curves aligned to the actual values, and by visualizing the fit of NARX prediction is smoother when compared with LSTM graphs and thus, the SNR results play a low priority role.

The research scope was limited to a single-vehicle test case leveraging the 2020 Cadillac CT5 datasets (Supplementary Tables S4–S7). Hence, specific snippets of data with ACCSSP = [30 75] MPH (Supplementary Table S15) were selected, and another validation check was performed for the developed NARX DL model. The plots of predicted [EOP, CATOP], comparing the actual values, were shown individually in Supplementary Figures S13–S17, Supplementary Figures S18–S22 (EAT > 65 °F), and Supplementary Figures S23–S27 (EAT < 45°F). The computational efficacy of prediction was projected using the STMV, as shown in Supplementary Tables S16 and S17. The IET RMSE values were <1.7, and IES FOD was <0.27 for all the datasets, whereas the IFCR SNR has an acceptable range of [6.36 985.73]. Similarly, the EST RMSE <2.3 (EAT > 65°F) and <0.9 (EAT <45°F), whereas ACRFP SNR has a range of [2.2 16.1]. Therefore, the efficacy of the NARX DL model was proven, and the results were assumed to be satisfactory. Also, an increased number of datasets and enhanced validation would enrich prediction precision. However, in this article, the core concept of IVDM was highlighted, and further elements of the research were pursued in Sections 6–9.

6.1. Estimation of future input states: Vehicle level vectors

Step 1: The empirical relations defined in Table 2 were utilized to estimate the future input values of vehicle level vectors ( $ {VLV}_{k+1} $ ) of the DL model relative to the current state of the vehicle ( $ {VLV}_k $ ) (Figure 5). The speed ( $ {S}_{k+1} $ ) was varied in the default range of AVS, and the parameter odometer ( $ {O}_{k+1} $ ) was calculated by basic linear interpolation using [ $ {S}_{k+1} $ , $ {L}_{o\left( k+1\right)} $ ] assuming a constant time-step. The magnitude of LOT ( $ {L}_{o\left( k+1\right)} $ ) was estimated by calculating the force required to overcome the resistance (rolling, gradient, aerodynamics) for maintaining the ACCSSP, assuming no-slip and tire pressure $ {TP}_{k+1} $ is same as the previous step measured in kilopascals (kPa). The rolling resistance was estimated using the coefficient ( $ {\mu}_r $ ), whereas aerodynamic resistance is calculated using the area of cross section, drag coefficient, and density of air [ $ {A}_c $ , $ {C}_d $ , $ {\rho}_a $ ] whose magnitudes were shown in Table 2. The steering parameters [LAT ( $ {L}_{a\left( k+1\right)} $ ),YAR ( $ {Y}_{a\left( k+1\right)} $ )] were estimated assuming ISB, whereas the cabin air temperature $ {CAT}_{k+1} $ is varied in the default range of AVC. The Cadillac is equipped with [ADAS, AICON] features (super and ultra-cruise), which would generate the vectors [RRC, gradient, EAT] = [ $ {RRC}_{k+1} $ , $ {\varTheta}_{k+1} $ , $ {EAT}_{k+1} $ ] based on the GPS coordinates, and thus $ {VLV}_{k+1} $ was estimated as shown in Table 3.

Table 3.

Estimated inputs—deep learning model (10 time steps = 1 s) (Kolachalama and Malik, Reference Kolachalama and Malik2021a,Reference Kolachalama and Malikb).

6.2. Prediction of output states: NARX DL model

Step 2: The input sets of vehicle level vectors ( $ {VLV}_{k+1} $ ) were estimated for all the values of the AVS range (e.g., [65, 75] MPH), and thus 11 sets of inputs were generated. These matrices were fed into the developed NARX DL model, and therefore a corresponding 11 sets of $ {EOP}_{k+1} $ were predicted. Similarly, six sets of $ {\mathrm{CATOP}}_{k+1} $ were predicted by varying the Cabin air temperatures in the allowable range AVC = [65 70]°F.

6.3. Implementation: Vehicle engine performance criteria

Step 3: The vehicle engine performance criteria defined in Section 4 were applied to the predicted vectors [ $ {EOP}_{k+1},{\mathrm{CATOP}}_{k+1} $ ], whose results for 10 time-steps were shown in Tables 4 and 5. The top six performing optimal ACC speeds and CAT’s were selected for each vehicle engine performance criteria, as shown in Tables 6 and 7. Among these values, the top three modes were selected as the eligible vehicle speeds (EVS) [71, 70, 69] MPH and eligible vehicle cabin air temperatures (EVC) ([68, 70, 67]°F or [72, 71, 70]°F) for the time step $ {T}_{k+1} $ . A similar process was implemented for 100-time steps, and therefore the [ACC Speed, CAT] matrix was framed as shown in Table 8.

Table 4.

Vehicle engine performance—Iteration of AVS (10 time steps = 1 s).

Table 5.

Vehicle engine performance—Iteration of AVC (10-time steps = 100 m).

Table 6.

Optimal ACC speeds—Vehicle engine performance (10 time steps = 1 s).

Table 7.

Optimal CAT values—Vehicle engine performance (10 time steps = 100 m).

Table 8.

Optimal ACC speed (10 s) and CAT matrix (1,000 m)—100-time steps.

6.4. Algorithm: [ACCSSP, CATSP] prediction

The [ACC, CAT] matrix was framed with three [EVS, EVC] for 10-time steps [1 s, 100 m]. Thus, a maximum of $ {3}^{10} $ [ACCSSP’s, CATSP’s] are possible for 100-time steps, and a unique [ACCSSP, CATSP] was generated by implementing the following algorithm.

Step 4A: Estimation of ACCSSP

1. 1. Assuming the [ACC speed, Speed limit] at $ {T}_k $ is [ $ {S}_k $ , SL], and if the EVS at $ {T}_{k+1} $ are either $ {S}_k $ +1, $ {S}_k $ , or $ {S}_k $ −1, then $ {S}_{k+1} $ = $ {S}_k $ +1.

2. 2. If the EVS at $ {T}_{k+1} $ is neither $ {S}_k $ +1, $ {S}_k $ , nor $ {S}_k $ −1, then $ {S}_{k+1}\hskip0.35em =\hskip0.35em {S}_k $ .

3. 3. If $ {S}_{k+1}\hskip0.35em =\hskip0.35em {S}_k $ for more than 100-time steps, $ {S}_{k+1} $ = $ {S}_k $ +1 if $ {S}_k $ +1 $ \le $ SL + 5 or $ {S}_{k+1} $ = $ {S}_k $ −1 if $ {S}_k $ = SL + 5.

Step 4B: Estimation of CATSP

1. 1. Assuming the CAT at $ {dT}_k $ is $ {C}_k $ , if the EVC at $ {dT}_{k+1} $ are either $ {C}_k $ +1, $ {C}_k $ , or $ {C}_k $ −1.

   Case 1: $ {EAT}_k\ge {EAT}_o{}^{\circ}\mathrm{F} $ , then $ {C}_{k+1} $ = $ {C}_k $ +1. ( $ {C}_k $ +1 $ \le $ $ maxCAT{}^{\circ}\mathrm{F} $ ).

1. 2. If the EVC at $ {dT}_{k+1} $ are neither $ {C}_k $ +1, $ {C}_k $ , nor $ {C}_k $ −1, then $ {C}_{k+1}\hskip0.35em =\hskip0.35em {C}_k $ . ( $ minCAT{}^{\circ}\mathrm{F} $ $ \le {C}_k $ $ \le $ $ maxCAT{}^{\circ}\mathrm{F} $ )

2. 3. If $ {C}_{k+1}\hskip0.35em =\hskip0.35em {C}_k $ for more than 100-time steps, then

   Case 1: $ {EAT}_k\ge {EAT}_o{}^{\circ}\mathrm{F} $ , then $ {C}_{k+1} $ = $ {C}_k $ +1, if $ {C}_k $ +1 $ \le $ $ maxCAT{}^{\circ}\mathrm{F} $ or $ {C}_{k+1} $ = $ {C}_k $ −1, if $ {C}_k\hskip0.35em =\hskip0.35em $ $ maxCAT{}^{\circ}\mathrm{F} $ .

The algorithm proposed in Step 4 [A, B] was implemented on [ACC Speed, CAT] matrices (Table 8), by unit step increment of [ $ {S}_0 $ , $ {C}_0 $ ] in the range of [AVS, AVC]. The possible [ACCSSP’s, CATSP’s] were shown in Figures 7a and 8a, and a unique [ACCSSP, CATSP] was obtained by assuming initial values of ACC Speed (IAS) and Cabin air temperature (ICAT) [IAS ( $ {S}_0 $ ), ICAT ( $ {C}_0 $ )] = [70 MPH, 65°F] as shown in Figures 7b and 8b.

Figure 7.

Unique ACCSSP generation—(a) AVS = [65 75] MPH and (b) Initial ACC speed = 70 MPH.

Figure 8.

Unique CATSP generation—(a) AVC = [65 70]°F and (b) Initial CAT = 65°F.