Risk-Aware Lane Selection on Highway with Dynamic Obstacles

TL;DR

This paper introduces a real-time, risk-aware lane selection algorithm for highway driving that considers dynamic obstacles and traffic flow, optimizing safety and travel time using a search-based method within a neural network-enhanced MPC framework.

Contribution

It presents a novel, modular, search-based lane selection algorithm that evaluates uncertain vehicle behaviors for improved safety and efficiency in highway scenarios.

Findings

Effective real-time lane selection in simulation

Improved safety and travel time metrics

Robustness to dynamic obstacle uncertainties

Abstract

This paper proposes a discretionary lane selection algorithm. In particular, highway driving is considered as a targeted scenario, where each lane has a different level of traffic flow. When lane-changing is discretionary, it is advised not to change lanes unless highly beneficial, e.g., reducing travel time significantly or securing higher safety. Evaluating such "benefit" is a challenge, along with multiple surrounding vehicles in dynamic speed and heading with uncertainty. We propose a real-time lane-selection algorithm with careful cost considerations and with modularity in design. The algorithm is a search-based optimization method that evaluates uncertain dynamic positions of other vehicles under a continuous time and space domain. For demonstration, we incorporate a state-of-the-art motion planner framework (Neural Networks integrated Model Predictive Control) under a CARLA simulation environment.