Table 4.
Notations used in the research.
ADAS Algorithm | |
---|---|
HWADAS (s) | Threshold headway used by the ADAS algorithm. It represents the smallest safe headway. represents the boundary of the threshold headway |
WD (m) | Warning distance, a threshold distance used by the ADAS algorithm. It represents the shortest safe spacing between a subject vehicle and its leading vehicle |
H | Subject vehicle that hosts the ADAS system |
L | Leading vehicle in front of the subject vehicle |
AHmax (m/s2) | Anticipated maximum deceleration of the subject driver |
A (m/s2) | Current acceleration |
V (m/s) | Current speed |
TLS (s) | Time for the leading vehicle to stop: TLS = -VL-AL |
TFS (s) | Time for the subject vehicle to stop: THS = T – (VH + AH · PRT) / AHmax if VH + AH · PRT > 0, or THS = -VH - AH |
RR (m/s) | Range rate, which equals to VL - VH |
D0 (m) | Minimum distance between the leading vehicle and the host vehicle |
TM (s) | Time when RR is 0: TM = {[RR + (AL - AH)PRT / (AHmax - AL)} + PRT if TM > PRT, otherwise TM = PRT |
WD* ADAS (m) | Boundary of the warning distance, a function of the WD |
Driver behavior parameters | |
σ | Compliance level, a random number with a range of 0–100 |
DH* (s) | Desired headway affected by the ADAS |
PRT* (s) | Perception-reaction time affected by the ADAS |
DH0 (s) | Desired headway of a driver not equipped with the ADAS |
PRT0 (s) | Perception-reaction time of a driver not equipped with the ADAS |
Δt1 (s) | ADAS influence time |
Δt2 (s) | Recovering time Car-following and lane-changing models |
ṿ (m/s2) | Acceleration of a modeled vehicle |
a (m/s2) | Maximum (acceptable) acceleration of a modeled driver |
b (m/s2) | Maximum (acceptable) deceleration of a modeled driver |
v0 (m/s) | Desired speed of a modeled driver |
v (m/s) | Actual speed of a modeled driver |
Δv (m/s) | Relative speed with the leading driver |
S* (m) | Desired following spacing |
s0 (m) | Spacing in jam traffic |
s (m) | Actual spacing between a subject driver and the leading driver |
α, b | Model coefficients in the intelligent driver model |