Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving

Ma, Liang; Xue, Jianru; Kawabata, Kuniaki; Zhu, Jihua; Ma, Chao; Zheng, Nanning

doi:10.1109/tits.2015.2389215

Cited by 159 publications

(76 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, RRT (rapidly exploring random tree) algorithm and its many variants are also gradually applied in intelligent vehicle path planning [23][24][25]. As a typical random sampling method, RRT can combine the dynamic constraints of the vehicle during the path planning.…”

Section: Rout Planningmentioning

confidence: 99%

A Survey of Cooperative Driving between Auxiliary Autonomous System and Human Driver

Zou

Zhang

et al. 2018

MATEC Web Conf.

View full text Add to dashboard Cite

Abstract. The cooperative driving is a main direction of intelligent vehicle development since safety-critical tasks must be executed by human and autonomous system. An intelligent vehicle is equipped with a full range of sensors, which are larger and faster than the perception and computational ability of a human driver, while the latter has a comprehensive ability to adapt to unexpected events. According to their respective advantages, the cooperative driving between human and autonomous system can have new synergies. The emphasis of this paper is to survey the current state of the art of cooperative driving, with a specific focus on decision-making and motion planning levels and correlative algorithms. Such researches enable the autonomous system to compensate the human driver in dangerous or uncomfortable circumstance. This paper provides insights into the scope of decision-making and motion planning for cooperative driving, as well as the shortcomings and tendencies.

show abstract

Section: Rout Planningmentioning

confidence: 99%

A Survey of Cooperative Driving between Auxiliary Autonomous System and Human Driver

Zou

Zhang

et al. 2018

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…The car also displayed passing ability on the road (Huang et al, 2010). Tsinghua University, Xi'an Jiaotong University, Hefei Institute of Physical Science of the Chinese Academy of Sciences, and other research institutes have also developed their own UVs (Zhao et al, 2012;Ma et al, 2015). From 2008 to 2015, the National Natural Science Foundation of China organized seven China Smart Car Future Challenges against the background of road traffic needs (Huang et al, 2014).…”

Section: Trends In Unmanned Vehicle Developmentmentioning

confidence: 99%

Current trends in the development of intelligent unmanned autonomous systems

Zhang

et al. 2017

Frontiers Inf Technol Electronic Eng

125

View full text Add to dashboard Cite

Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.

show abstract

“…Like state lattice methods, the optimality of the solution depends on the resolution of sampling. Although samplingbased methods, such as rapidly-exploring random tree (RRT) (Kuwata et al, 2009;Ma et al, 2015), have been implemented to work in real time on powerful computers, they cannot run on simple microprocessors (Glaser et al, 2010), which are more likely and feasible to be applied on commercial vehicles in the near future and will be used in this paper for algorithm simulation. Parametric planning methods model the trajectories with parametric expressions, e.g., polynomials (Papadimitriou and Tomizuka, 2003) and clothoids (Köhler et al, 2013), where the unknown parameters are optimized to compute the trajectory solution.…”

Section: Related Workmentioning

confidence: 99%

“…Driverless vehicles, such as the vehicles competing in the DARPA Urban Challenge (Montemerlo et al, 2008;Urmson et al, 2008) and the autonomous vehicle A1 (Chu et al, 2012), navigate autonomously in complex environments (e.g., highways) with only on-board sensors and computers. For either of these applications, trajectory planning is a fundamental and important technology as it can be stated as "computing a sequence of control values or feasible movement states for the vehicle to maneuver among obstacles from an initial state toward a desired terminal state, taking into account the vehicle's kinematic and dynamic model" (Ma et al, 2015). This paper focuses on on-road trajectory planning, which deals with the generation of trajectories in the urban road environment.…”

Section: Introductionmentioning

confidence: 99%

Intelligent computing budget allocation for on-road trajectory planning based on candidate curves

Jiang

Huang

et al. 2016

Frontiers Inf Technol Electronic Eng

View full text Add to dashboard Cite

In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation (ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolution (OODE). The proposed algorithm is named IOODE with 'I' representing ICBA. OODE plans the trajectory in two parts: trajectory curve and acceleration profile. The best trajectory curve is picked from a set of candidate curves, where each curve is evaluated by solving a subproblem with the differential evolution (DE) algorithm. The more iterations DE performs, the more accurate the evaluation will become. Thus, we intelligently allocate the iterations to individual curves so as to reduce the total number of iterations performed. Meanwhile, the selected best curve is ensured to be one of the truly top curves with a high enough probability. Simulation results show that IOODE is 20% faster than OODE while maintaining the same performance in terms of solution quality. The computing budget allocation framework presented in this paper can also be used to enhance the efficiency of other candidate-curve-based planning methods.

show abstract

Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving

Cited by 159 publications

References 30 publications

A Survey of Cooperative Driving between Auxiliary Autonomous System and Human Driver

A Survey of Cooperative Driving between Auxiliary Autonomous System and Human Driver

Current trends in the development of intelligent unmanned autonomous systems

Intelligent computing budget allocation for on-road trajectory planning based on candidate curves

Contact Info

Product

Resources

About