This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Data-Driven Confidence Model for ADAS Object Detection
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
The majority of road accident is due to human error. Advanced Driver Assistance System (ADAS) has the potential to reduce human error and improve driving safety. Customers have shown a growing acceptance for ADAS technology. With the rising demand for safety and comfortable driving experience, the global market for ADAS is expected to grow to $67 billion by 2025.
A reliable ADAS system requires an accurate and robust object-detection system. There is often a trade-off in tuning the system. On one hand, miss-detection can cause accidents; on the other hand, false-detection can result in ghost-braking and harm the driving experience. The ADAS system can access various information from different sources. However, a unified confidence model, which combines different indicators, has not been much studied in the literature. In this paper, we propose a data-driven method, which utilizes the features from radar, camera and the tracking system to produce a high-level confidence model. In addition, different regions regarding the ego vehicle usually have different emphases for detection error based on the system design requirements. And therefore, we can tune towards the design requirements by change the threshold of the classifier based on the region of interest.
The proposed method was validated with real-world driving data and shown a better performance based on the design requirement of the Adaptive Cruise Control (ACC) and Autonomous Emergency Braking (AEB) functions.
CitationYang, H., Zhang, D., Wang, D., and Zhou, J., "Data-Driven Confidence Model for ADAS Object Detection," SAE Technical Paper 2020-01-0695, 2020, https://doi.org/10.4271/2020-01-0695.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
|[Unnamed Dataset 3]|
|[Unnamed Dataset 4]|
|[Unnamed Dataset 5]|
- Stanton, N.A. and Salmon, P.M. , “Human Error Taxonomies Applied to Driving: A Generic Driver Error Taxonomy and Its Implications for Intelligent Transport Systems,” Safety Science 47(2):227-237, 2009.
- Online article, “Path-to-Autonomous-Driving-Begins-with-Consumer-and-Adas-Adoption,” Retrieved from https://www.aptiv.com/media/article/path-to-autonomous-driving-begins-with-consumer-and-adas-adoption.
- Grand View Research , Advanced Driver Assistance Systems (ADAS) Market Size, Share & Trend Analysis Report By Solution (Adaptive Cruise Control, Blind Spot Detection), By Component, By Vehicle, “And Segment Forecasts,” 2018-2025.
- Zhong, Z. et al. , “Camera Radar Fusion for Increased Reliability in ADAS Applications,” Electronic Imaging 2018(17):258-251, 2018.
- SAE On-Road Automated Vehicle Standards Committee , “Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems,” SAE Standard J 3016:1-16, 2014.
- Zhou, Y., et al. , “Image-Based Vehicle Analysis Using Deep Neural Network: A Systematic Study,” in 2016 IEEE International Conference on Digital Signal Processing (DSP), IEEE, 2016.
- Ekström, L., and Risberg, J. , “Sensor Fusion of Radar and Stereo-Vision for Tracking Moving Vehicles as Extended Objects.”
- Chavez-Garcia, R.O. , “Multiple Sensor Fusion for Detection, Classification and Tracking of Moving Objects in Driving Environments,” PhD diss., Université de Grenoble, 2014.
- Serfling, M. et al. , “Camera and Imaging Radar Feature Level Sensorfusion for Night Vision Pedestrian Recognition,” in 2009 IEEE Intelligent Vehicles Symposium, IEEE, 2009.
- Ji, Z., and Prokhorov D. , “Radar-Vision Fusion for Object Classification,” in 2008 11th International Conference on Information Fusion, IEEE, 2008.
- Kämpchen, N. , “Feature-Level Fusion of Laser Scanner and Video Data for Advanced Driver Assistance Systems,” Diss, Universität Ulm, 2007.
- Viktorová, L. and Šucha, M. , “Drivers’ Acceptance of Advanced Driver Assistance Systems-What to Consider,” International Journal for Traffic and Transport Engineering 8(3):320-333, 2018.
- Standard, ISO 26262-1 , “Road Vehicles - Functional Safety.”
- Lee, S.-I., Lee, H., Abbeel, P., and Andrew, Y.N. , “Efficient L~ 1 Regularized Logistic Regression,” AAAI 6:401-408, 2006.
- Otto, C. et al. , “A Joint Integrated Probabilistic Data Association Filter for Pedestrian Tracking across Blind Regions Using Monocular Camera and Radar,” in 2012 IEEE Intelligent Vehicles Symposium, IEEE, 2012.
- Burkard, R.E. and Cela, E. , “Linear Assignment Problems and Extensions,” . In: Handbook of Combinatorial Optimization. (Boston, MA, Springer, 1999), 75-149.
- Altendorfer, R. , “Observable Dynamics and Coordinate Systems for Automotive Target Tracking,” in 2009 IEEE Intelligent Vehicles Symposium, IEEE, 2009.
- Nordenmark, V., and Forsgren, A. , “Radar-Detection Based Classification of Moving Objects Using Machine Learning Methods,” 2015.
- Rusu, R.B. , “Semantic 3d Object Maps for Everyday Manipulation in Human Living Environments,” KI-Künstliche Intelligenz 24(4):345-348, 2010.