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A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety

Pengfei Lin, Ehsan Javanmardi, Yuze Jiang, Manabu Tsukada

TL;DR

A rule-compliance path planner for lane-merge scenarios, which initially employs the extended responsibility-sensitive safety (RSS) to elucidate the right-of-way, followed by the potential field-based sigmoid planner for path generation, which demonstrated superior performance over previous approaches in aspects such as merging time, path length, and eliminating the trajectory oscillation.

Abstract

Lane merging is one of the critical tasks for self-driving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards in measuring the capability of autonomous driving systems. However, due to the ambiguity in driving intentions and right-of-way issues, the lane merging process in autonomous driving remains deficient in terms of maintaining or ceding the right-of-way and attributing liability, which could result in protracted durations for merging and problems such as trajectory oscillation. Hence, we present a rule-compliance path planner (RCPP) for lane-merge scenarios, which initially employs the extended responsibility-sensitive safety (RSS) to elucidate the right-of-way, followed by the potential field-based sigmoid planner for path generation. In the simulation, we have validated the efficacy of the proposed algorithm. The algorithm demonstrated superior performance over previous approaches in aspects such as merging time (Saved 72.3%), path length (reduced 53.4%), and eliminating the trajectory oscillation.

A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety

TL;DR

A rule-compliance path planner for lane-merge scenarios, which initially employs the extended responsibility-sensitive safety (RSS) to elucidate the right-of-way, followed by the potential field-based sigmoid planner for path generation, which demonstrated superior performance over previous approaches in aspects such as merging time, path length, and eliminating the trajectory oscillation.

Abstract

Lane merging is one of the critical tasks for self-driving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards in measuring the capability of autonomous driving systems. However, due to the ambiguity in driving intentions and right-of-way issues, the lane merging process in autonomous driving remains deficient in terms of maintaining or ceding the right-of-way and attributing liability, which could result in protracted durations for merging and problems such as trajectory oscillation. Hence, we present a rule-compliance path planner (RCPP) for lane-merge scenarios, which initially employs the extended responsibility-sensitive safety (RSS) to elucidate the right-of-way, followed by the potential field-based sigmoid planner for path generation. In the simulation, we have validated the efficacy of the proposed algorithm. The algorithm demonstrated superior performance over previous approaches in aspects such as merging time (Saved 72.3%), path length (reduced 53.4%), and eliminating the trajectory oscillation.
Paper Structure (8 sections, 8 equations, 5 figures)

This paper contains 8 sections, 8 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Rule-Compliance Path Planning
  4. Extended Responsibility-Sensitive Safety for Merging
  5. Potential Field
  6. Sigmoid Planner
  7. Simulation Results
  8. Conclusion

Figures (5)

  • Figure 1: Overall system framework with RCPP: the mapping database and sensors can send raw data to the vehicle-to-vehicle (V2V) communication, external perception, and localization. After that, the featured data is sent to the planning layer, which includes the RSS, the PF, and the sigmoid planner. The reddish-brown solid line indicates that the RSS can enforce commands if emergencies are sensed. Then, the control layer will deliver the commands to the actuation.
  • Figure 2: Merging situations with different traffic conditions
  • Figure 3: Proposed sigmoid planner based on the PF under the RSS criteria
  • Figure 4: Lane-merge paths with TriPField-based Planner, Non-cooperative RCPP Planner, and Cooperative Planner
  • Figure 5: Motion states of the ego and obstacle vehicles

Theorems & Definitions (2)

  • Definition 1: Non-cooperative Lane Merge
  • Definition 2: Cooperative Lane Merge