Exploring the Limitations of Behavior Cloning for Autonomous Driving

The evolution of autonomous driving has transitioned from rule-based systems to deep learning approaches, with early works like Pomerleau’s lane following (1989) pioneering imitation learning. Recent advances include CNN-based models such as NVIDIA’s PilotNet, which demonstrated end-to-end control in static environments. Simulation platforms like CARLA (Dosovitskiy et al., 2017) have enabled scalable testing of complex scenarios. Despite progress, challenges remain in generalizing to dynamic, unstructured urban settings, especially when models trained on static datasets encounter unseen traffic patterns and rare events. The industry seeks solutions that combine robustness, safety, and scalability, pushing research toward multi-task architectures and richer simulation environments.

Current behavior cloning methods struggle to generalize beyond training distributions, especially in dense traffic and unpredictable scenarios. Data bias leads models to overfit common patterns, failing in rare or complex interactions like sudden pedestrian crossings or multi-vehicle maneuvers. Additionally, training instability and high variance due to initialization and sampling order hinder reproducibility and robustness. These issues limit the deployment of imitation-based systems in real-world urban driving, where safety and reliability are paramount. Addressing these bottlenecks requires new evaluation standards, model architectures, and training paradigms that can handle the inherent complexity and variability of real traffic.

The paper introduces CILRS, a multi-task deep network combining ResNet34 with a speed prediction branch, designed to improve stability and generalization. The new NoCrash benchmark evaluates models in highly dynamic, dense traffic scenarios, emphasizing safety-critical behaviors. By leveraging large-scale simulation data, the approach demonstrates superior performance over prior methods like CAL, MT, and CIRL, especially in unseen environments. The architecture’s multi-task design encourages the perception module to encode rich scene dynamics, reducing reliance on dataset biases. This integrated framework marks a significant step toward scalable, robust imitation learning for urban autonomous driving.

• �� Data collection: Over 400 hours of expert driving data in CARLA, filtered for anomalies, forming the CARLA100 dataset.
• �� Model architecture: ResNet34 backbone for perception, with branches for control and speed prediction, enabling multi-task learning.
• �� Training: Supervised with L1 loss, using Adam optimizer, with dropout regularization, and ImageNet pretraining to reduce variance.
• �� Evaluation: Success rate and infractions measured across static, dynamic, and unseen scenarios, comparing multiple model variants.
• �� Analysis: Variance and ablation studies to understand stability, data bias effects, and the impact of multi-task learning.

The models were trained on subsets of the dataset, validated on separate scenes, and tested on both CARLA and NoCrash benchmarks. Experiments included varying training data size, initialization, and sampling order to assess stability. The evaluation focused on success rate, collision frequency, and rule violations across static and dynamic scenarios. Results showed that the proposed CILRS architecture achieved success rates above 97% in many tasks, outperforming prior methods by 10-26%. The experiments highlighted the persistent challenge of generalizing in dense traffic, with performance degradation under unseen conditions, emphasizing the importance of model robustness.

CILRS achieved success rates exceeding 97% in static and moderate traffic scenarios, with a 20-30% improvement in generalization to new environments over prior methods. In dense traffic, performance dropped by 55-66%, indicating ongoing challenges. The model demonstrated robustness in lateral and longitudinal control, with significant gains attributed to the multi-task design and large-scale training. Variance analysis revealed sensitivity to initialization and sample order, suggesting avenues for further stability improvements. Overall, the results validate the architecture’s effectiveness and highlight the importance of comprehensive evaluation in simulation.

The developed models can be integrated into urban autonomous driving systems, providing end-to-end control with high safety standards. They are suitable for deployment in city environments, where dynamic interactions are frequent. The approach can also serve as a foundation for semi-supervised or online learning systems, continuously improving through real-world data. Long-term, these advancements could enable fully autonomous vehicles to operate safely in complex traffic, reducing accidents and improving traffic flow, ultimately transforming urban mobility.

Despite notable progress, the models still face difficulties in dense traffic and rare event scenarios, indicating limited robustness. Data bias and overfitting reduce performance in unseen environments. Training instability and high variance hinder reproducibility. Future work should incorporate causal inference, diverse data augmentation, and online adaptation to enhance robustness. Additionally, transferring models from simulation to real-world remains a significant challenge, requiring further research into sim-to-real transfer techniques.