Evolving an intelligent vehicle for tactical reasoning in traffic

Sukthankar, Rahul; Baluja, Shumeet; Hancock, John

doi:10.1109/robot.1997.620089

Cited by 15 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, experts believe it would be best to only automate some of the tasks that arise while driving, but to leave the most complex tasks to a human driver for the time being. According to Sukthankar et al ( 1997 ), the task of driving consists of different levels, which are the strategic level (route planning), the tactical level (maneuver selection), and the operational level (maneuver operation). Automation of the lowest, operational level is thus legally the least complex, and also technically possible (Dickmanns and Zapp, 1987 ; Pomerleau, 1992 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving

Zander

Andreessen

Berg

et al. 2017

Front. Hum. Neurosci.

View full text Add to dashboard Cite

We tested the applicability and signal quality of a 16 channel dry electroencephalography (EEG) system in a laboratory environment and in a car under controlled, realistic conditions. The aim of our investigation was an estimation how well a passive Brain-Computer Interface (pBCI) can work in an autonomous driving scenario. The evaluation considered speed and accuracy of self-applicability by an untrained person, quality of recorded EEG data, shifts of electrode positions on the head after driving-related movements, usability, and complexity of the system as such and wearing comfort over time. An experiment was conducted inside and outside of a stationary vehicle with running engine, air-conditioning, and muted radio. Signal quality was sufficient for standard EEG analysis in the time and frequency domain as well as for the use in pBCIs. While the influence of vehicle-induced interferences to data quality was insignificant, driving-related movements led to strong shifts in electrode positions. In general, the EEG system used allowed for a fast self-applicability of cap and electrodes. The assessed usability of the system was still acceptable while the wearing comfort decreased strongly over time due to friction and pressure to the head. From these results we conclude that the evaluated system should provide the essential requirements for an application in an autonomous driving context. Nevertheless, further refinement is suggested to reduce shifts of the system due to body movements and increase the headset's usability and wearing comfort.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving

Zander

Andreessen

Berg

et al. 2017

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…(9) can be evaluated by draw ing Ns particles from the process noise, measurement noise, initial state, and discrete mode at each time- …”

Section: A Convex Bounding Methodsmentioning

confidence: 99%

“…The work in [9] proposes a machine learning approach for tactical reasoning in traffic situations such as passing maneuvers and merging into a traffic stream. A hybrid control approach to semiautonomous multivehicle safety is developed in [6].…”

Section: Electrical Engineering and Computer Sciencesmentioning

confidence: 99%

A probabilistic approach to planning and control in autonomous urban driving

Vitus

Tomlin

2013

52nd IEEE Conference on Decision and Control

View full text Add to dashboard Cite

This paper considers the problem of decision mak ing and control for autonomous urban vehicles operating among other non-cooperating, possibly human controlled, vehicles. The difficulty in this problem stems from the fact that the behavior of the other vehicles is uncertain, and in many circumstances a collision cannot be prevented even under restrictive assumptions about the other drivers' actions. Tr aditional approaches that consider the worst-case actions of the other vehicles typically are inapplicable because the solutions are either too conserva tive or infeasible. This work proposes a new method for solving this problem using a chance constrained framework, which requires that the violation probability of all the constraints is guaranteed to be below a threshold. This formulation has the benefit of not only achieving traditional objectives such as minimization of fuel or travel time, but also hedges the nominal planned maneuver against potential crashes leading from the uncertainty in the other drivers' actions. The proposed framework is demonstrated on a passing maneuver example which exhibits a region where the passing vehicle cannot be guaranteed to prevent a collision in all circumstances.

show abstract

“…Ref. 30 provides details of the evolutionary algorithm, and presents results showing the robustness of this strategy.…”

Section: Automated Parameter Tuningmentioning

confidence: 99%

<title>Distributed tactical reasoning framework for intelligent vehicles</title>

Sukthankar

Thorpe

1998

SPIE Proceedings

View full text Add to dashboard Cite

In independent vehicle concepts for the Automated Highway System (AHS), the ability to make competent tacticallevel decisions in real-time is crucial. Traditional approaches to tactical reasoning typically involve the implementation of large monolithic systems, such as decision trees or finite state machines. However, as the complexity of the environment grows, the unforeseen interactions between components can make modifications to such systems very challenging. For example, changing an overtaking behavior may require several, non-local changes to car-following, lane changing and gap acceptance rules. This paper presents a distributed solution to the problem. PolySAPIENT consists of a collection of autonomous modules (termed "reasoning objects"), each specializing in a particular aspect of the driving task -classified by traffic entities rather than tactical behavior. Thus, the influence of the vehicle ahead on the available actions is managed by one reasoning object, while the implications of an approaching exit are managed by another. The independent recommendations from these reasoning objects are expressed in the form of votes and vetos over a "tactical action space", and are resolved by a voting arbiter. This local independence enables PolySAPIENT reasoning objects to be developed independently, using a heterogeneous implementation (e.g., obstacle avoidance modules can use potential fields while exit reasoning is performed using fuzzy rules). PolySAPIENT vehicles are implemented in the SHIVA tactical highway simulator, whose vehicles are based on the Carnegie Mellon Navlab robots.

show abstract

Evolving an intelligent vehicle for tactical reasoning in traffic

Abstract: Abstract

Cited by 15 publications

References 9 publications

Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving

Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving

A probabilistic approach to planning and control in autonomous urban driving

<title>Distributed tactical reasoning framework for intelligent vehicles</title>

Contact Info

Product

Resources

About