Dynamic Probabilistic Drivability Maps for Lane Change and Merge Driver Assistance

Abstract

In this paper, we present a novel probabilistic compact representation of the on-road environment, i.e., the dynamic probabilistic drivability map (DPDM), and demonstrate its utility for predictive lane change and merge (LCM) driver assistance during highway and urban driving. The DPDM is a flexible representation and readily accepts data from a variety of sensor modalities to represent the on-road environment as a spatially coded data structure, encapsulating spatial, dynamic, and legal information. Using the DPDM, we develop a general predictive system for LCMs. We formulate the LCM assistance system to solve for the minimum-cost solution to merge or change lanes, which is solved efficiently using dynamic programming over the DPDM. Based on the DPDM, the LCM system recommends the required acceleration and timing to safely merge or change lanes with minimum cost. System performance has been extensively validated using real-world on-road data, including urban driving, on-ramp merges, and both dense and free-flow highway conditions.

Motorcycle detection for ADAS through camera and V2V Communication, a comparative analysis of two modern technologies

“…The visual interface was developed based on squares such as Sivaraman and Trivedi (2014) and Diewald, Móller, Roalter, and Kranz (2012), where the screen is divided into 5x5 squares (Fig. 6), and the vehicle (a car in this case) is placed in the central cell (cell 3.3); the space in front of the vehicle is represented by rows 1 and 2, and the rear positions are represented by rows 4 and 5.…”

Structuring Cooperative Behavior Planning Implementations for Automated Driving

“…He assumes a communication channel to communicate vehicle state variables and maneuvers. Sivaraman et al [27] demonstrate an assistance system for lane changes to address the scenarios SC 1 and SC 5. Schwarting et al [28] propose a system to consider costs of other vehicles in a cooperative group and tests the approach in a simulation environment and offline with recorded data form an automated vehicle driving on a highway.…”

“…For this, the vehicle drove a stretch of 550 miles on a highway from Stanford to Considering (dis-)comfort costs for rear vehicles [20], [24], [31], [32], [34], [35], [27], [29], [30], [ Dedicated handling of zipper method merging where the automated vehicle merges or let merge [25], [16] / SC 10 Off-centered driving to leave space for obstructing objects [23], [37], [29], [30], [33], [38] SC 11…”

Overtaking & receding vehicle detection for driver assistance and naturalistic driving studies

“…Vehicle detection [2], [3], [4], [5], lane detection [6], [7], [8], pedestrian detection [9] and higher order tasks involving lanes and vehicles such as trajectory analysis [10], [11], [12] using different sensing modalities, is therefore an active area of research for automotive active safety systems, and in offline data analysis for naturalistic driving studies (NDS) also [13], [14], [15].…”