Autonomous Driving Test Videos:

In-house Developed Drive-By-Wire (DBW) Kit for New Cars via Hacking

In-house Developed Drive-By-Wire (DBW) Kit for Old Cars via Adding External Actuators

Reinforcement Learning  (RL) and Optimal Control in Autonomous Driving 01/2021- present

Research topics:

• Full stack SAE Level 2/3 ADAS software development and vehicle testing (Featuring environment testing, behavior prediction, path planning, path tracking)

• ADAS algorithm light-weighting for onboard computing resource minimization

• End to end learning based path planning

• point to point navigation

• vision-based ADAS vehicle testing

• Closed course and on-road ADAS testing

• Inverse Reinforcement Learning (IRL) based path planning

• Lyapunov optimization based path planning

• Model Predictive Control (MPC) based path planning

• Vehicle Drive-by-Wire (DBW) modification including steering, braking, and accelerating actuators control by wire

• Autonomous Vehicle testing including Automated Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Centering Assist (LCA), and Point to Point Navigation.

Featured Publication:

• Z. Arjmandzadeh, M. H. Abbasi, H. Wang, J. Zhang, and B. Xu, “A Lyapunov Optimization-Based Approach to Autonomous Vehicle Local Path Planning,” Sensors, vol. 24, pp. 8031, 2025. (PDF)

• Z. Arjmandzadeh, M. H. Abbasi, H. Wang, J. Zhang, and B. Xu, “A Comparative Study on Autonomous Vehicle Local Path Planning Through Model Predictive Control and Frenet Frame Method,” SAE International Journal of Connected and Automated Vehicles, vol. 7, no. 12-07-04–0029, 2024, Accessed: Dec. 16, 2024. [Online]. Available: https://www.sae.org/publications/technical-papers/content/12-07-04-0029/. (PDF)

• Xu, B., Rathod, D., Zhang, D.R., Yebi, A., Zhang X.Y., Li, X.Y., Filipi, Z., “Parametric Study on Reinforcement Learning Optimized Energy Management Strategy for a Hybrid Electric Vehicle”, Applied Energy, vol.259, p.114200, 2020. (PDF)

Reinforcement Learning  (RL) in Propulsion Systems 01/2014- present

Research topics:

• RL function mechanism interpretation based on hybrid propulsion system energy management application

• Learning time reduction of RL via warm start (e.g., value/ policy functions initialization with expert knowledge or conventional control)

• Adaptiveness investigation of RL over various environmental and vehicular conditions (e.g., activation and deactivation mechanism of adaptiveness process)

• Ensemble RL (e.g., combination of various RL algorithms and conventional controls to improve robustness and optimality of the results)

• RL Convergence study via recent Basal Ganglia Neuroscience studies (e.g., new computational model for RL learning end process, which will replace existing learning rate)

• Unifying theories of tonic (action) and phasic (learning) Dopamine release considering neuroscience experimental study.

• Investigation of sensory, learning, and motoring circuit in Basal Ganglia by combining neuroscience experimental data and computational models.

Featured Publication:

• Xu, B., Rathod, D., Zhang, D.R., Yebi, A., Zhang X.Y., Li, X.Y., Filipi, Z., “Parametric Study on Reinforcement Learning Optimized Energy Management Strategy for a Hybrid Electric Vehicle”, Applied Energy, vol.259, p.114200, 2020. (PDF)

• Xu, B., Hu, X.S., Lin, X.K., Li, H.Y., Rathod, D., Filipi, Z., “Ensemble Reinforcement Learning as a Hybrid Electric Vehicle Supervisory Control for Fuel Economy Improvement”, IEEE Transactions on Transportation Electrification, 2020, doi: 10.1109/TTE.2020.2991079. (PDF)

• Xu, B., Hou, J., Shi, J., Li, H., Wang, Z., Rathod, D., and Filipi, Z., “Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control Strategy in Hybrid Electric Vehicle Applications”, IEEE Transactions on Transportation Electrification, 2020, doi: 10.1109/TTE.2020.3019009. (PDF)

• Xu, B., J. Shi, S. Li, H. Li, and Z. Wang, “Energy consumption and battery aging minimization using a Q-learning strategy for a battery/ultracapacitor electric vehicle,” Energy, vol. 229, p. 120705, 2021. (PDF)

• Xu, B., Tang, X.L., Hu, X.S., Lin, X.K., Li, X.Y., Rathod, D., and Wang, Z., “Q-learning Based Supervisory Control Adaptability Investigation for Hybrid Electric Vehicles, IEEE Transactions on Intelligent Transportation Systems, 2021, doi: 10.1109/TITS.2021.3062179. (PDF)

• Xu, B., and Li, X.Y., “A Q-learning Based Transient Power Optimization Method for Organic Rankine Cycle Waste Heat Recovery System in Heavy Duty Diesel Engine Applications”, Applied Energy, 2021, doi: https://doi.org/10.1016/j.apenergy.2021.116532. (PDF)

Supervised Learning in Propulsion Systems 09/2017-04/2020

Research topics:

• Development of Data-Driven Lithium-ion battery design assistant tool by building machine learning models connecting vehicle specification and battery design parameters

• Real-time realization of offline Dynamic Programming optimization results by utilizing machine learning models to extract optimal rules from Dynamic Programming results

• Multi-layer hierarchical ensemble machine learning to improve the prediction accuracy

Featured Publication:

• Xu, B., Rathod, D., Yebi, A., Onori, S., Filipi, Z., “Real-Time Realization of Dynamic Programming Using Machine Learning Methods for IC Engine Waste Heat Recovery System Power Optimization”, Applied Energy, vol.262, p.114514, 2020. (PDF)

• Xu, B., Rizzo, D., and Onori, S., “Machine Learning Based Optimal Energy Storage Devices Selection Assistance for Vehicle Propulsion Systems", SAE 2020 World Congress & Exhibition, 2020. (PDF)

• Xu, B., Zhang, D., and Tang, S., “Malware Classification Utilizing Supervised Learning in Autonomous Driving Applications”, SAE - 19th Asian Pacific Automotive Engineering Conference (APAC), Shanghai, China, 2017. (PDF)