Merge recommendations for driver assistance: A cross-modal, cost-sensitive approach

Abstract

In this study, we present novel work focused on assisting the driver during merge maneuvers. We use an automotive testbed instrumented with sensors for monitoring critical regions in the vehicle's surround. Fusing information from multiple sensor modalities,we integrate measurements into a contextually relevant, intuitive, general representation, which we term the Dynamic Probabilistic Drivability Map [DPDM]. We formulate the DPDM for driver assistance as a compact representation of the surround environment, integrating vehicle tracking information, lane information, road geometry, obstacle detection, and ego-vehicle dynamics. Given a robust understanding of the ego-vehicle's dynamics, other vehicles, and the on-road environment, our system recommends merge maneuvers to the driver, formulating the maneuver as a dynamic programming problem over the DPDM, searching for the minimum cost solution for merging. Based on the configuration of the road, lanes, and other vehicles on the road, the system recommends the appropriate acceleration or deceleration for merging into the adjacent lane, specifying when and how to merge.

Control Concepts for Facilitating Motorway On-ramp Merging Using Intelligent Vehicles

“…Finally, Sivaraman et al (2013) developed a recommendation system to assist the merging into motorways. A vehicle was equipped with cameras, radar and LIDAR sensors, capable of capturing infrastructural characteristics such as lane and obstacle, and positions and speeds of surrounding vehicles.…”

“…Few researchers have evaluated the algorithm performance in the case of mixed traffic (van Arem et al, 2006;Davis, 2007;Pueboobpaphan et al, 2010;Sivaraman et al, 2013;Wei et al, 2013;Xu & Sengupta, 2003). It is unrealistic to assume a complete and instantaneous switch in the fleet to intelligent vehicles; therefore, the evaluation of the transition period is crucial.…”

“…of runs Evaluated indexes Not stated Not stated Not stated Gap and speed profiles Kachroo and Li (1997) MATLAB (MathWorks, 2013) Not stated Not stated Gap and speed profiles Smart-AHS (Antoniotti & Gll, 1997) USA Not stated Gap and speed profiles, queue Uno et al (1999) Not stated Not stated Not stated Speed profiles Ran et al (1999) CORSIM (Halati, Lieu, & Walker, 1997) USA Not stated Average main carriageway and merging speed Hall and Li (2001) Not stated USA 10 Average waiting time in merging queue, average merging queue length Kato et al (2002) (test track) Xu and Sengupta (2003) MATLAB (MathWorks, 2013) USA Not stated Speed and acceleration profiles, average speed and maximum braking Lu et al (2004) (test track) van Arem et al (2006) MIXIC ( Awal et al (2013b) kFTM (Awal, Kulik & Ramamohanrao, 2013a) Not stated Not stated Merging time, waiting time, energy consumption, flow and average speed Sivaraman et al (2013) (test track) Wei et al (2013) Not stated Not stated 10 000 Costs associated with desired distance, accelerations, breaking distance and fuel consumption…”

Semantic state space for high-level maneuver planning in structured traffic scenes

“…Occupancy grids are often equidistant like the Bayesian occupancy grids in [1]. However, in [2] for example road data is used to adapt the grid to the current road geometry. Usually there are no objects or object classes.…”

Integrating motion and appearance for overtaking vehicle detection

“…Additionally, incorporating long term motion trajectories, as in [18], could improve the overall performance of the algorithm. Furthermore, it might prove useful to integrate our overtaking vehicle detector into other driver assistance systems, such as a system for merge recommendations [19].…”