Objective testing of a cooperative intersection collision avoidance system for traffic signal and stop sign violation

Maile, Michael; Ahmed-Zaid, F.; Bai, Sue; Caminiti, L; Mudalige, Priyantha; Peredo, Michael; Popović, Željko

doi:10.1109/ivs.2011.5940431

Cited by 16 publications

(6 citation statements)

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“…The uncertainty in the state at each node is represented as an uncertainty ellipse. If the probability of collision is too high, the node is discarded (red); otherwise the node is kept (green) and may be used to grow future trajectories Violations (CICAS-V) project (Maile et al (2008)). The obtained results demonstrated that RR-GP reduced prediction errors by almost a factor of 2 when compared to two standard GP-based algorithms, while maintaining computation times that are suitable for real-time implementation.…”

Section: Computation Timesmentioning

confidence: 99%

Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

et al. 2013

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This paper presents a real-time path planning algorithm that guarantees probabilistic feasibility for autonomous robots with uncertain dynamics operating amidst one or more dynamic obstacles with uncertain motion patterns. Planning safe trajectories under such conditions requires both accurate prediction and proper integration of future obstacle behavior within the planner. Given that available observation data is limited, the motion model must provide generalizable predictions that satisfy dynamic and environmental constraints, a limitation of existing approaches. This work presents a novel solution, named RR-GP, which builds a learned motion pattern model by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation. Obstacle trajectory GP predictions are conditioned on dynamically feasible paths identified from the reachability analysis, yielding more accurate predictions of future behavior. RR-GP predictions are integrated with a robust path planner, using chance-constrained RRT, to identify probabilistically feasible paths. Theoretical guarantees of probabilistic feasibility are shown for linear systems under Gaussian uncertainty; approximations for nonlinear dynamics and/or non-Gaussian uncertainty are also presented. Simulations demonstrate that, with this planner, an autonomous vehicle can safely navigate a complex environment in realtime while significantly reducing the risk of collisions with dynamic obstacles.

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Section: Computation Timesmentioning

confidence: 99%

Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

et al. 2013

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“…The data that potentially contain the best knowledge of drivers' true behaviors are the naturalistic driving data [41]- [44]. Naturalistic driving data are collected when the vehicles are driven under natural conditions, and data collection lasts a long period of time [14] (e.g, 12 to 13 months in the 100-Car Naturalistic Driving Study [41]).…”

Section: A Naturalistic Driving Datasetmentioning

confidence: 99%

Driving Stability Analysis Using Naturalistic Driving Data With Random Matrix Theory

et al. 2020

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Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed. INDEX TERMS Driving behavior analysis, intelligent transportation systems, random matrix theory.

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“…Recently in Europe, some of the local public transportation vehicles have started to communicate with a certain number of traffic lights [7]. In the USA, industry and academia are conducting experiments in broadcasting red light timings for security warning systems [8]. These advances in communication systems, sensor technology, and high performance computation sources enable further work in driving profile optimization, an approach which still holds a great potential for energy consumption reduction of road vehicles [9], [10] at a very limited cost.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution

Ozatay

Onori

Wollaeger

et al. 2014

IEEE Trans. Intell. Transport. Syst.

243

121

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Driving style, road geometry, and traffic conditions have a significant impact on vehicles' fuel economy. In general, drivers are not aware of the optimal velocity profile for a given route. Indeed, the global optimal velocity trajectory depends on many factors, and its calculation requires intensive computations. In this paper, we discuss the optimization of the speed trajectory to minimize fuel consumption and communicate it to the driver. With this information the driver can adjust his/her speed profile to reduce the overall fuel consumption. We propose to perform the computation-intensive calculations on a distinct computing platform called the "cloud." In our approach, the driver sends the information of the intended travel destination to the cloud. In the cloud, the server generates a route, collects the associated traffic and geographical information, and solves the optimization problem by a spatial domain dynamic programming (DP) algorithm that utilizes accurate vehicle and fuel consumption models to determine the optimal speed trajectory along the route. Then, the server sends the speed trajectory to the vehicle where it is communicated to the driver. We tested the approach on a prototype vehicle equipped with a visual interface mounted on the dash of a test vehicle. The test results show 5%-15% improvement in fuel economy depending on the driver and route without a significant effect on the travel time. Although this paper implements the speed advisory system in a conventional vehicle, the solution is generic, and it is applicable to any kind of powertrain structure.

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Objective testing of a cooperative intersection collision avoidance system for traffic signal and stop sign violation

Cited by 16 publications

References 0 publications

Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

Driving Stability Analysis Using Naturalistic Driving Data With Random Matrix Theory

Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution

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