Operating autonomous vehicles at the absolute limits of handling requires precise, real-time identification of highly non-linear tire dynamics. However, traditional online optimization methods suffer from "cold-start" initialization failures and struggle to model high-frequency transient dynamics. To address these bottlenecks, this paper proposes a novel vision-augmented, iterative system identification framework. First, a lightweight CNN (MobileNetV3) translates visual road textures into a continuous heuristic friction prior, providing a robust "warm-start" for parameter optimization. Next, a S4 model captures complex temporal dynamic residuals, circumventing the memory and latency limitations of traditional MLPs and RNNs. Finally, a derivative-free Nelder-Mead algorithm iteratively extracts physically interpretable Pacejka tire parameters via a hybrid virtual simulation. Co-simulation in CarSim demonstrates that the lightweight vision backbone reduces friction estimation error by 76.1 using 85 fewer FLOPs, accelerating cold-start convergence by 71.4. Furthermore, the S4-augmented framework improves parameter extraction accuracy and decreases lateral force RMSE by over 60 by effectively capturing complex vehicle dynamics, demonstrating superior performance compared to conventional neural architectures.
Figures & tables
Fig. 1 : Schematic representation of the dynamic single-track model.
Fig. 2 : Architecture of the vision-based friction estimation module. The system utilizes a MobileNetV3-Small backbone across four hierarchical stages for efficient feature extraction. The Inverted Residual Block topology incorporates a SE attention mechanism for texture recalibration. The Probabilistic Friction Mapper executes a classification-to-regression mapping, weighting the Softmax distribution p by the physical basis vector B to derive the continuous expected friction coefficient μ^ .
Fig. 3 : Architecture of the S4 model. The diagram illustrates the transition from HiPPO -initialized continuous state-space to a discrete convolutional representation, enabling parallelized computation of vehicle dynamic residuals.
Fig. 4 : Iterative framework for tire model identification.
Fig. 5 : Visualization of the data acquisition phase in the CarSim environment.
Metric
MobileNetV3 (ours)
ResNet-18 [ 9 ]
EfficientNet [ 19 ]
Params (M)
1.52
11.18
4.01
FLOPs (G)
0.12
3.65
0.83
RMSE
0.102
0.427
0.310
Latency (ms)
5.91
5.33
8.35
TABLE I : Quantitative comparison of candidate vision backbones.
Metric
No Vision
Vision
Init. Friction μ0
0.51
0.51
Vision Friction μ^
–
0.80
Fyf/Fzf RMSE
0.219
0.076
Fyr/Fzr RMSE
0.073
0.046
Iter.
7
2
TABLE II : Effect of Vision Prior on Identification Initialization
Fig. 6 : Comparison of identified tire force curves with and without the vision prior. The vision prior significantly improves the initial parameter accuracy.
Architecture
Fyf/Fzf RMSE
Fyr/Fzr RMSE
Time [s]
MLP
0.234
0.071
5.3
RNN
0.097
0.084
12.9
S4 (Ours)
0.051
0.033
12.2
TABLE III : Performance Metrics of Different Residual Architectures for Parameter Extraction
Fig. 7 : Comparison of identified Pacejka tire force curves using different neural architectures. The proposed S4 model achieves the highest identification accuracy and best alignment with the ground truth.