Effective Perception System Design Through Accurate Performance Prediction and Resource Optimization
通过准确的性能预测和资源优化进行有效的感知系统设计
基本信息
- 批准号:RGPIN-2021-03881
- 负责人:
- 金额:$ 2.04万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2021
- 资助国家:加拿大
- 起止时间:2021-01-01 至 2022-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
In an autonomous vehicle, data from radar, video and acoustic sensors are used for collision avoidance and safe navigation. In a border security system, radar and video sensors are used to improve the safety and security of citizens. In both autonomous and surveillance systems, a critical feature is perception: the ability to automatically absorb sensor data, understand the scene therein, identify the salient aspects in the scene, alert human operators of any impending dangers, and take measures to ensure the safety of people and property. Since such safety-critical autonomous vehicles and surveillance systems demand high precision and real-time performance, it is essential to use the best possible sensors, subject to cost or physical constraints, and to get the best performance out of the available sensor resources. While current autonomous and surveillance systems have come a long way, the perception modules therein are still susceptible to failures, mistakes and accidents, as evidenced by aircraft disappearances, vehicle accidents, border breaches and mining disasters. The long-term objective of the proposed research program is to develop end-to-end algorithms to improve and perfect the perception module in autonomous vehicles and surveillance systems so as to improve the quality of life and safety of citizens who increasingly depend on such systems. The program will ensure that the perception module provides the best possible information to the human user (or the autonomous control system) who will then use that information to obtain the best possible outcome in a critical scenario, such as an impending accident. Optimal perception requires the optimal selection, coordination and use of sensors. However, resource management in real-time, while the autonomous system is operational, is challenging due to imperfect sensors, computational complexity and conflicting priorities. Resource optimization at design-time, where design choices are often made through costly trial-and-error processes, is inefficient. Sub-optimal decisions, without accurate perception performance prediction during pre-production design or real-time operation, can increase the cost of autonomous systems, adversely affect their efficiency and jeopardize the safety of users. Thus, the short-term goals of the research proposed here are to 1) derive accurate performance prediction techniques under realistic operational conditions that can be calculated at design-time or in real-time to improve the design and operation of safety-critical perception systems; and 2) develop algorithms for optimal design-time and real-time perception resource usage based on accurate performance prediction. The proposed work, through collaboration with Canadian automotive industry and government agencies, will reduce the cost of perception modules, make them more accurate and efficient, and make the overall system safer, thus improving the safety, security, and quality of life of Canadians.
在自动驾驶车辆中,来自雷达、视频和声音传感器的数据用于避免碰撞和安全导航。在边境安全系统中,雷达和视频传感器被用来提高公民的安全和安保。在自主和监视系统中,一个关键功能是感知:自动吸收传感器数据、了解其中的场景、识别场景中的突出方面、警告人类操作员任何迫在眉睫的危险并采取措施确保人员和财产安全的能力。由于这种安全关键的自动驾驶车辆和监控系统要求高精度和实时性能,因此在成本或物理限制的情况下,使用尽可能好的传感器并从可用的传感器资源中获得最佳性能是至关重要的。虽然目前的自主和监测系统已经取得了长足的进步,但其中的感知模块仍然容易出现故障、错误和事故,如飞机失踪、车辆事故、越界和矿难。拟议研究计划的长期目标是开发端到端算法,以改进和完善自动驾驶车辆和监控系统中的感知模块,以提高越来越依赖此类系统的公民的生活质量和安全。该程序将确保感知模块向人类用户(或自主控制系统)提供可能的最佳信息,然后用户将使用该信息在关键场景中获得可能的最佳结果,例如迫在眉睫的事故。最佳感知需要传感器的最佳选择、协调和使用。然而,由于传感器不完善、计算复杂和优先级冲突,在自治系统运行的同时,实时资源管理是具有挑战性的。在设计时进行资源优化是低效的,在设计时,设计选择通常是通过昂贵的反复试验过程做出的。在生产前设计或实时运行过程中,如果没有准确的感知性能预测,次优决策可能会增加自治系统的成本,影响其效率,危及用户的安全。因此,本文提出的研究的短期目标是:1)在实际运行条件下获得准确的性能预测技术,这些技术可以在设计时或实时计算,以改进安全关键感知系统的设计和操作;2)基于准确的性能预测,开发优化设计时间和实时感知资源使用的算法。这项拟议的工作将通过与加拿大汽车行业和政府机构的合作,降低感知模块的成本,使其更加准确和高效,并使整个系统更安全,从而提高加拿大人的安全、保障和生活质量。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Tharmarasa, Ratnasingham其他文献
Online clutter estimation using a Gaussian kernel density estimator for multitarget tracking
- DOI:
10.1049/iet-rsn.2014.0037 - 发表时间:
2015-01-01 - 期刊:
- 影响因子:1.7
- 作者:
Chen, Xin;Tharmarasa, Ratnasingham;McDonald, Mike - 通讯作者:
McDonald, Mike
Passive Tracking in Heavy Clutter With Sensor Location Uncertainty
- DOI:
10.1109/taes.2016.140820 - 发表时间:
2016-08-01 - 期刊:
- 影响因子:4.4
- 作者:
Guo, Yunfei;Tharmarasa, Ratnasingham;Kirubarajan, Thia - 通讯作者:
Kirubarajan, Thia
A Practical Bias Estimation Algorithm for Multisensor-Multitarget Tracking
- DOI:
10.1109/taes.2015.140574 - 发表时间:
2016-02-01 - 期刊:
- 影响因子:4.4
- 作者:
Taghavi, Ehsan;Tharmarasa, Ratnasingham;McDonald, Michael - 通讯作者:
McDonald, Michael
A Multidimensional TDOA Association Algorithm for Joint Multitarget Localization and Multisensor Synchronization
- DOI:
10.1109/taes.2019.2943786 - 发表时间:
2020-06-01 - 期刊:
- 影响因子:4.4
- 作者:
Ge, Tongyu;Tharmarasa, Ratnasingham;Kirubarajan, Thiagalingam T. - 通讯作者:
Kirubarajan, Thiagalingam T.
A Track Quality Based Metric for Evaluating Performance of Multitarget Filters
- DOI:
10.1109/taes.2013.6404124 - 发表时间:
2013-01-01 - 期刊:
- 影响因子:4.4
- 作者:
He, Xiaofan;Tharmarasa, Ratnasingham;Thayaparan, Thayananthan - 通讯作者:
Thayaparan, Thayananthan
Tharmarasa, Ratnasingham的其他文献
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{{ truncateString('Tharmarasa, Ratnasingham', 18)}}的其他基金
Effective Perception System Design Through Accurate Performance Prediction and Resource Optimization
通过准确的性能预测和资源优化进行有效的感知系统设计
- 批准号:
RGPIN-2021-03881 - 财政年份:2022
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Effective Perception System Design Through Accurate Performance Prediction and Resource Optimization
通过准确的性能预测和资源优化进行有效的感知系统设计
- 批准号:
DGECR-2021-00223 - 财政年份:2021
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Launch Supplement
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