Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles

Chu, Keonyup; Lee, Minchae; Sunwoo, Myoungho

doi:10.1109/tits.2012.2198214

Cited by 297 publications

(145 citation statements)

References 28 publications

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“…Gehrig and Stein [11] used the notion of elastic bands attached to the vehicle being followed to showcase vehicle following behavior. Similarly Chu et al [12] used different possibilities in the construction of some candidate paths out of which the best path was used for the actual vehicle navigation. These works were aimed at showcasing limited vehicular behaviors however.…”

Section: Related Researchmentioning

confidence: 99%

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Kala

Warwick

2015

Electronics

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Autonomous vehicles operate in real time traffic scenarios and aim to reach their destination from their source in the most efficient manner possible. Research in mobile robotics provides a variety of sophisticated means with which to plan the path of these vehicles. Conversely professional human drivers usually drive using instinctive means, which enables them to reach their goal almost optimally whilst still obeying all traffic laws. In this paper we propose the use of fuzzy logic for novel motion planning. The planner is generated using an evolutionary algorithm which resembles the learning stage of professional drivers. Whether to overtake or not, is a decision which affects one's driving and the decision is made using some deliberation. We further extend the approach to perform decision making regarding overtaking for all vehicles. Further we coordinate the motion of the vehicles at a traffic crossing to avoid any potential jam or collision. Experimental results prove that by using this approach we have been able to make the vehicles move in an optimal manner in a variety of scenarios.

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Section: Related Researchmentioning

confidence: 99%

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Kala

Warwick

2015

Electronics

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“…In [1][2][3][4][5], various lane detection algorithms and path-planning algorithms are proposed for vehicles to realize the autonomous driving. The driving behavior of vehicles driving on the road, however, is always affected by other vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…The driving behavior of vehicles driving on the road, however, is always affected by other vehicles. The autonomous driving in [1][2][3][4][5], however, is standalone. The driving decision information of other vehicles is not considered.…”

Section: Introductionmentioning

confidence: 99%

Common Driving Notification Protocol Based on Classified Driving Behavior for Cooperation Intelligent Autonomous Vehicle Using Vehicular Ad-Hoc Network Technology

Lin

Dong

Hirota

2015

Journal of Artificial Intelligence and Soft Computing Research

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A protocol, called common driving notification protocol (CDNP), is proposed based on the classified driving behavior for intelligent autonomous vehicles, and it defines a standard with common messages and format for vehicles. The common standard format and definitions of CDNP packet make the autonomous vehicles have a common language to exchange more detail driving decision information of various driving situations, decrease the identification time for one vehicle to identify the driving decisions of other vehicles before or after those driving decisions are performed. The simulation tools, including NS-3 and SUMO, are used to simulate the wireless data packet transmission and the vehicle mobility; the experiment results present that the proposed protocol, CDNP, can increase the reaction preparing time with maximum value 250 seconds, decrease the identification time and the average travel time. Prospectively, it is decided to implement the CDNP as a protocol stack in the Linux kernel to provide the basic protocol capability for real world transmission testing.

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“…Kuwata et al [17] used Rapidly-exploring Random Trees for the planning of a single vehicle using a biased sampling technique for obstacle avoidance. Chu et al [18] constructed a number of trajectories for obstacle avoidance using a single manoeuver, out of which the best was used. Gehrig and Stein [19] used an elastic band attached to a leader vehicle to model the motion of the follower vehicles.…”

Section: Introductionmentioning

confidence: 99%

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Kala

Warwick

2014

Appl Intell

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Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.

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Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles

Cited by 297 publications

References 28 publications

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Common Driving Notification Protocol Based on Classified Driving Behavior for Cooperation Intelligent Autonomous Vehicle Using Vehicular Ad-Hoc Network Technology

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

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