Deep Model Compression Techniques for Intelligent Automotive Cockpits

The concept of intelligent vehicles has become mainstream, and the vehicle cockpit is a key component in this scenario. Intelligent cockpits improve passenger comfort and driver experience, but most core algorithms rely on deep‑learning visual models that are large and computationally intensive. Limited on‑device resources (CPU, memory, storage) make it difficult to run multiple models in real time.

This article introduces the principles and methods of model compression and their application in automotive cockpit scenarios, focusing on three main techniques: pruning, quantization, and knowledge distillation.

1. Model Pruning

Pruning removes redundant weights from a deep network, reducing parameter count and computational cost. Both unstructured (weight‑level) and structured (filter, channel, or shape) pruning are discussed. Structured pruning can directly delete entire convolution kernels or input channels, which aligns with the GEMM (general matrix‑multiply) representation used during inference. The article describes an ADMM‑based pruning framework that formulates pruning as a constrained optimization problem and solves it via alternating updates of weights, regularization targets, and dual variables. The ADMM core algorithm and its convergence criteria are presented with the corresponding mathematical expressions.

2. Quantization

Quantization reduces the bit‑width of model weights, typically converting 32‑bit floating‑point values to 16‑bit floating‑point (FP16) or 8‑bit integer (INT8). The uniform quantization process maps the floating‑point range to a discrete integer range using a scale and zero‑point. Post‑training quantization and quantization‑aware training are compared, highlighting the trade‑off between accuracy loss and implementation simplicity.

3. Knowledge Distillation

Knowledge distillation transfers knowledge from a large teacher network to a compact student network. The loss function combines a soft loss (cross‑entropy between student and teacher soft predictions) and a hard loss (cross‑entropy with ground‑truth labels), enabling the small model to retain high accuracy.

4. Automated Structured Pruning – AutoCompress

Building on the ADMM pruning method, the AutoCompress framework automatically searches pruning hyper‑parameters (e.g., pruning ratio) using a simulated‑annealing‑style search. It evaluates candidate configurations by quickly estimating weight or FLOPs reduction without full retraining, then refines the best candidates with ADMM‑based structured pruning.

5. Application to Intelligent Cockpit – Driver Monitoring System (DMS)

The compressed models are deployed in an edge‑cloud hybrid driver‑monitoring system. Lightweight models run on the in‑vehicle terminal to extract fatigue features, while the cloud performs temporal analysis to detect fatigue states. This pipeline reduces latency and enables real‑time alerts, significantly decreasing fatigue driving time.

In summary, model compression—through pruning, quantization, and knowledge distillation—makes it feasible to run multiple deep‑learning tasks on resource‑constrained automotive cockpits while preserving accuracy and meeting real‑time requirements.