Odin: Team VictorTango’s Entry in the DARPA Urban Challenge

Reinholtz, Charles F.; Hong, Dennis; Wicks, A. L.; Bacha, Andrew; Bauman, Cheryl; Faruque, Ruel; Fleming, Michael; Terwelp, Chris; Alberi, Thomas; Anderson, David E.; Cacciola, Stephen; Currier, Patrick; Dalton, Aaron; Farmer, Jesse; Hurdus, Jesse W.; Kimmel, Shawn; King, Peter; Taylor, Ashby B.; Covern, David Van; Webster, Mike

doi:10.1007/978-3-642-03991-1_4

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…Whereas the first two Grand Challenges focused on off-road autonomous driving, DARPA's 2007 Urban Challenge was the first to be held in an urban setting. Held in Victorville, California, this challenge required 11 participating teams to design their own autonomous vehicles that could navigate through an urban environment containing marked lanes, traffic signs and signals, and other vehicles while obeying the rules of the road (Reinholtz et al, 2007). Following DARPA's Urban Challenge, a number of organizations began to host their own challenges in Europe (McEachern, 2015), Asia (Hyundai Motor Group, 2014), the United States (SAE International, 2021), and virtually (Shell, 2021).…”

Section: Non-racing Autonomous Challengesmentioning

confidence: 99%

Assessment of an Autonomous Racing Controller: A Case Study From the Indy Autonomous Challenge ’ s Simulation Race

Kim

Cazares

Decker

et al. 2023

The Journal of Technology, Management, and Applied Engineering

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The Indy Autonomous Challenge (IAC) was a competition among universities from around the world created to showcase fully autonomous operations under the extreme circumstances encountered in high-performance racing. The pinnacle of the virtual portion of the competition occurred on June 30,2021, during the IAC Simulation Race that consisted of a series of qualifying events followed by a head-to-head race. The objectives of this paper are to present a team’s controller and demonstrate its performance throughout the IAC until the final simulation race. The results presented in this paper were obtained by testing the team’s controller within a simulated environment. The final controller is capable of sustaining speeds of nearly 300 kph (186 mph) with an average speed of 280 kph(174 mph) and a maximum speed of 298 kph (185 mph). The final steering proportional-integral-derivative controller yielded cross-track errors no more than 4.7 meters from the desired waypoint. Analysis of the simulated vehicle’s G-G diagram reveals that the vehicle could sustain operations while experiencing over 2.5 Gs of lateral force as it navigated the turns of the track, and there is evidence to suggest that future work can be performed to tap into the full potential of the tires’ grip capabilities.The results presented in this report are indicative that the race team’s controller can perform safe high-speed operations in the presence of seven additional script-controlled opponents within a simulated environment.

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Section: Non-racing Autonomous Challengesmentioning

confidence: 99%

Assessment of an Autonomous Racing Controller: A Case Study From the Indy Autonomous Challenge ’ s Simulation Race

Kim

Cazares

Decker

et al. 2023

The Journal of Technology, Management, and Applied Engineering

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“…Reinholt et al [202] describe the base vehicle and controls development of the perception-, and planning systems of the Odin autonomous robotic vehicle (a Ford Escape forming the basis). Perception uses laser scanners, GPS, and a priori knowledge to identify obstacles, cars, and roads.…”

Section: Autonomous Ground Vehiclesmentioning

confidence: 99%

Autonomous vehicle control systems — a review of decision making

Veres

Molnár

Lincoln

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part I:

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A systematic review is provided on artificial agent methodologies applicable to control engineering of autonomous vehicles and robots. The paper focuses on some fundamentals that make a machine autonomous: decision making that involves modelling the environment and forming data abstractions for symbolic processing and logic-based reasoning. Most relevant capabilities such as navigation, autonomous path planning, path following control, and communications, that directly affect decision making, are treated as basic skills of agents. Although many autonomous vehicles have been engineered in the past without using the agentoriented approach, most decision making onboard of vehicles is similar to or can be classified as some kind of agent architecture, even if in a naïve form. First the ANSI standard of intelligent systems is recalled then a summary of the fundamental types of possible agent architectures for autonomous vehicles are presented, starting from reactive, through layered, to advanced architectures in terms of beliefs, goals, and intentions. The review identifies some missing links between computer science results on discrete agents and engineering results of continuous world sensing, actuation, and path planning. In this context design tools for 'abstractions programming' are identified as needed to fill in the gap between logic-based reasoning and sensing. Finally, research is reviewed on autonomous vehicles in water, on the ground, in the air, and in space with comments on their methods of decision making. One of the main conclusions of this review is that standardization of decision making through agent architectures is desirable for the future of intelligent vehicle developments and their legal certification.

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“…Layered -hierarchical state machine based planners [7] 3. Strategic -logic selection based planners [79]. These planners performed reasonably well for the scenarios they were tuned for, but were seen to be inefficient and led to many failures during the testing when the scenario was not clearly perceived.…”

Section: Behaviour Plannersmentioning

confidence: 99%