No abstract

The Indy Autonomous Challenge (IAC) was a competition among universities from around the world created to showcase fully autonomous operations under the extreme circumstances encountered in high-performance racing. The pinnacle of the virtual portion of the competition occurred on June 30,2021, during the IAC Simulation Race that consisted of a series of qualifying events followed by a head-to-head race. The objectives of this paper are to present a team’s controller and demonstrate its performance throughout the IAC until the final simulation race. The results presented in this paper were obtained by testing the team’s controller within a simulated environment. The final controller is capable of sustaining speeds of nearly 300 kph (186 mph) with an average speed of 280 kph(174 mph) and a maximum speed of 298 kph (185 mph). The final steering proportional-integral-derivative controller yielded cross-track errors no more than 4.7 meters from the desired waypoint. Analysis of the simulated vehicle’s G-G diagram reveals that the vehicle could sustain operations while experiencing over 2.5 Gs of lateral force as it navigated the turns of the track, and there is evidence to suggest that future work can be performed to tap into the full potential of the tires’ grip capabilities.The results presented in this report are indicative that the race team’s controller can perform safe high-speed operations in the presence of seven additional script-controlled opponents within a simulated environment.

Safety is a critical aspect of transportation design and operations. Practitioners utilize various references to ensure that roadways meet safety, operational, and sustainability requirements. Despite this, human error remains as a contributing factor toward unsafe driving behavior and potential crashes. Connected and autonomous vehicles (CAVs) have the potential to enhance traffic safety and operations. Although sensor perception ranges and capabilities pose challenges, the sharing of information via Vehicle-to-Everything (V2X) communication provides CAVs with a potential solution for overcoming sensor limitations. The objective of this study is to use the Simulation of Urban Mobility software to assess safety impacts when using V2X to share sensor-obtained roadway information with a CAV. To this end, this study proposes a novel method for simulating driver behavior that combines car following with consideration of the roadway’s geometric configuration. Several scenarios are utilized to observe the behavior of simulated drivers on a straight tangent approaching a sharp horizontal curve. This study evaluates driver performance using the measured values for longitudinal jerk, lateral jerk, and speed variance. The results of this study indicate that V2X sensor sharing can provide significant benefits to CAV performance and can reduce the safety risk. CAVs receiving sensor-obtained information behave in a manner more akin to their human-driven counterparts in comparison to those receiving basic safety messages. CAVs using sensor-obtained information maintain braking and lateral jerk values within safety thresholds. In addition, speed variance was at its lowest when CAVs utilized V2X sensor information.