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Table 1 Steering behavior recognition

From: Chassis Coordinated Control for Full X-by-Wire Vehicles-A Review

Reference numbering

Algorithms

Inputs

Outputs

[33]

Rule-based

\(\delta , \, \dot{\delta }\)

Lane change intention

[42]

Rule-based

δ, v, γ, ax, vch

Straight run/cornering;

Stable/unstable

[34]

Fuzzy-based

ax, ay, az

Straight run/cornering;

Accelerate/decelerate

[36]

HMM-based

\(\delta , \, \dot{\delta }\)

Lane keeping/lane change

[44]

HMM-based

\(\delta , \, \dot{\delta }\)

Normal driving/emergency driving

[35]

HMM-based

ax, ay, v

Emergency warning

[37]

HMM-based

Lane position, δ, a, braking intensity, v, γ

Lane keeping/lane change Accelerate/decelerate

[38]

HMM-based

Lane position, ax, ay, v

Straight run/cornering

Accelerate/decelerate

[46]

R-HSMM-based

δ, ay

Lane change intention

[48]

Machine learning-based

ax, ay, az

Straight run/cornering

[49]

Machine learning-based

δ, v

Lane change

[40]

Supervised learning-based

Lane position, ax, ay

Lane keeping/lane change

[41]

Sequence learning-based

Signals, video images

Straight run/cornering

[51, 56]

Machine learning-based

Smartphone navigation

Straight run/cornering

[52]

Machine learning-based

Gaze accumulation and glance duration and frequency

Lane change intention

[53]

Deep learning-based

Six-axis sensor

Straight run/cornering

Normal/abnormal

[54]

Deep learning-based

a, v, δ

Straight run/cornering

Accelerate/decelerate

[55]

Model-based

GNSS receiver, gyro, accelerometer and odometry.

Straight running/cornering

Accelerate/decelerate

[57]

HMM-based

Eye fixations, speed and δ

Lane change intention