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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