SBIR Phase I: Mapping Driver Behavior: Toward an Active Safety Knowledge Base

该提案中提出的创新包括一个数据库，该数据库可以捕获和描述道路网络中任何一点的车辆预期行为。在特定道路上的驾驶行为范围，例如速度和横向位置，可以作为其他驾驶员和系统的参考，以提高驾驶安全性和效率。偏离正常范围可能表明驾驶员存在风险，需要培训机会或需要车载驾驶员辅助系统立即干预。该项目结合了两个创新功能。第一个是使用车辆数据作为建立数字地图的主要来源。第二个特点是表现行为而不是当前数字地图关注的物理道路结构，这对于预期的应用具有显著的优势。为了实现这一功能，该项目将展示一种新的核心地图架构，允许准确捕捉机动和行为。这几乎是不可能使用目前可用的地图格式。 在这个项目中，将建立一个简单的驾驶行为数据库，并将验证所提出的数据表示可以有效地构建和扩展，以覆盖大的地理区域。所提出的创新的更广泛/商业影响将是驾驶安全和效率的显着提高。捕捉行为允许他们的分析，以评估风险和确定最佳策略。这使得驾驶员能够获得反馈，以推动或积极地将他们推向更安全，更有效的模式。该数据库将满足几个运输市场的需求和应用：1）驾驶员辅助系统触发基于标称机动参数和驾驶员的个性化警告？习惯的风格; 2）车队和保险公司的风险评估-识别离群驾驶员，提供培训/反馈并设定保险费率; 3）为自动驾驶车辆系统提供输入，以设定对人类舒适并被其他驾驶员理解的轨迹; 4）基础设施管理人员的道路安全-观察车辆运动中的扰动，以诊断问题并设计补救升级;以及5）识别减少燃料使用和提高电动车辆的行驶里程的驾驶实践。这些都是具有巨大社会价值和收入潜力的大型目标市场。拟议的数据库可以单独从车辆数据自动构建，与现有的地图数据库相比，大大降低了成本。 还可以捕获新颖且有价值的道路和驾驶属性。

The innovation put forth in this proposal consists of a database that captures and describes expected behavior of vehicles at any point of a road network. The range of driving behaviors on a particular road, e.g. speed and lateral position, can serve as a reference for other drivers and systems to increase driving safety and efficiency. Deviations from the normal range may indicate a risky driver, training opportunities, or the need for immediate intervention from on-board driver assistance systems. The project brings together two innovative features. The first one is the use of vehicle data as a primary source to build a digital map. The second feature lies in representing behaviors rather than the physical road structure that current digital maps focus on. This has significant advantages for the intended applications. To enable this feature, this project will demonstrate a novel core map architecture that allows the accurate capture of maneuvers and behaviors. This is nearly impossible using currently available map formats. In this project a simple driving behavior database will be built and will validate that the proposed data representation can be effectively constructed and scaled to cover large geographic areas.The broader/commercial impact of the proposed innovation will be significant gains in driving safety and efficiency. Capturing behaviors allows their analysis to assess risk and identify optimal maneuvers. This enables feedback to drivers to either nudge or actively pull them toward safer and more efficient patterns. This database will address needs and applications in several transportation markets: 1) driver assistance systems triggering personalized warnings based on nominal maneuver parameters and driver?s habitual style; 2) risk assessment for fleets and insurance companies- identifying outlier drivers, providing training/feedback and setting insurance rates; 3) providing inputs to autonomous vehicle systems to set trajectories that are comfortable for humans and are understood by other drivers; 4) roadway safety for infrastructure managers- observing perturbations in vehicle movements to diagnose problems and engineer remedial upgrades; and 5) identifying driving practices that reduce fuel usage and improve the range of electric vehicles. These are large target markets with considerable societal value and revenue potential. The proposed database can be built automatically from vehicle data alone, significantly reducing costs compared to existing map databases. Novel and valuable roadway and driving attributes can also be captured.