Figure - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
The quantification and effective representation of safety risks for scenarios in structured road traffic environments of autonomous driving are currently being investigated in an active way. Based on artificial potential fields, a risk-field model for the traffic environment that considers the motion state of an obstacle vehicle is established, and...
Context in source publication
Context 1
... the path-generation section, the blue path is the candidate path planned by the path-planning algorithm; the red path is the optimal path selected by the conventional cost function, and the purple path is the optimal path selected by the cost function established in this study. Table 4 shows the algorithm parameter values in the algorithm verification process. Figure 7a shows a simulation scenario in which self-vehicle EGO1 traverses straight at a uniform speed of 60 km/h, and VE1 traverses straight at 60 km/h, with an acceleration of −0.6 m/s 2 . ...Similar publications
Motion planning of autonomous vehicles is still challenging in urban road environments with occlusions. In this work, with the view of safety, comfort and efficiency, we present a motion planning framework that enables autonomous vehicles to navigate safely in urban road with occlusions. Our solution mainly includes three parts: local path planning...
Citations
... The impact of safety risk factors on travel should be considered when making path planning [24]. The greater the number of vehicles n ij in the certain link Link ij , the greater the traffic load to the road network, and the greater the possibility of traffic accidents ε ij . ...
... The impact of safety risk factors on travel should be considered when mak planning [24]. The greater the number of vehicles in the certain link greater the traffic load to the road network, and the greater the possibility o accidents . ...
Vehicle–road coordination is an important application scenario in the sustainable development of urban transportation. In this scenario, by navigating the vehicles in the road network, the vehicles can run more smoothly in the city, reduce unnecessary detours and parking, and realize energy savings and emission reductions. Although vehicle–road coordination in a large area has not been fully realized, people’s travel is increasingly dependent on navigation. If the trips of most vehicles follow the same navigation suggestion in a short period of time, some sections in the given route of the navigation will bear excessive traffic load. In order to solve this potential problem, this paper relies on the vehicle–road collaboration scenario and combines the service level of the road network factors between vehicles to plan the travel path of the vehicle. This keeps the traffic load of each road section in the path at a reasonable level. Within the scope, considering the overall utilization of road resources and the efficiency of road network traffic, we established the road network evaluation index through the simulation comparison with the Dijkstra algorithm. Under the path planning method proposed in this paper, the total travel time of the vehicle is reduced by 23.4%, and the road network operation efficiency is improved by 6.6%, which proves that the method can be used. This method can effectively alleviate the load of the road network, improve operation efficiency, and finally achieve the purpose of energy saving and emission reduction.
